The City University of New York Committee on Academic Policy, Programs and Research

THE CITY UNIVERSITY OF NEW YORK COMMITTEE ON ACADEMIC POLICY, PROGRAMS AND RESEARCH

AGENDA April 15, 2019

I. Action Items

A. Approval of the minutes of the February 25, 2019 meeting

B. Policy Calendar

1. New York City College of Technology – BS in Data Science

2. LaGuardia Community College – AS in Physical Sciences

3. City College – BS in Science Learning & Public Engagement

4. City College – Resolution to Award an Honorary Doctor of Humane Letters to John Robert Lewis at the College's Commencement

5. City College – Resolution to Award an Honorary Doctor of Science to Edward S. Plotkin '53 at the College's Commencement

6. Lehman College – Resolution to Award an Honorary Doctor of Humane Letters to Ivan G. Seidenberg '72 at the College's Commencement

7. Lehman College – Resolution to Award an Honorary Doctor of Humane Letters to Dr. Mildred García (NYCCT '71, BAR '74) at the College's Commencement

8. Lehman College – Resolution to Award an Honorary Doctor of Fine Arts to Whitfield Lovell at the College's Commencement

9. Baruch College – Resolution to Award an Honorary Doctor of Laws to Max Berger (CCNY '68) at the College's Commencement

10. Baruch College – Resolution to Award an Honorary Doctor of Letters to President Danilo Medina Sánchez at the College's Commencement

11. Brooklyn College – Resolution to Award an Honorary Doctor of Humane Letters to Tarana Burke at the College's Commencement

12. Medgar Evers College – Resolution to Award an Honorary Doctor of Humane Letters to Alfred Sharpton, Jr. at the College's Commencement

13. CUNY School of Public Health and Health Policy - Resolution to Award an Honorary Doctor of Science to Dr. David Nabarro at the School's Commencement

14. CUNY Graduate School and University Center - Resolution to Award an Honorary Doctor of Humane Letters to Luis Miranda, Jr. at the School's Commencement

15. CUNY Graduate School and University Center - Resolution to Award an Honorary Doctor of Humane Letters to Roxane Gay at the College's Commencement

16. Macaulay Honors College - Resolution to Award an Honorary Doctor of Humane Letters to Aasif Hakim Mandviwala (Aasif Mandvi) at the College's Commencement

17. The College of Staten Island - Resolution to Award an Honorary Doctor of Humane Letters, to Jane Kurtin at the College's Commencement

18. Queens College – Resolution to Award an Honorary Doctor of Humane Letters to Dina Perry '67, '71 at the College's Commencement

19. Queens College – Resolution to Award an Honorary Doctor of Humane Letters to Reri Grist '54 at the College's Commencement

20. Queens College - Resolution to Award an Honorary Doctor of Humane Letters to Dr. Miriam Rafailovich (BC '75) at the College's Commencement

21. The City University of New York - Resolution to Award an Honorary Doctor of Humane Letters to Matthew Higgins '98 at Queens College's Commencement

22. John Jay College - Resolution to Award an Honorary Doctor of Laws to the Honorable Loretta Lynch at the College's Commencement

23. John Jay College - Resolution to Award an Honorary Doctor of Laws to the Honorable Alex Calabrese at the College's Commencement

24. CUNY School of Law - Resolution to Award an Honorary Doctor of Laws to Martha S. Jones (HC '83, Law '87) at the School's Commencement

25. CUNY Office of Academic Affairs – Approval of the CAPPR Report

BOARD OF TRUSTEES THE CITY UNIVERSITY OF NEW YORK

COMMITTEE ON MINUTES OF THE MEETING ACADEMIC POLICY, PROGRAMS AND RESEARCH FEBRUARY 25, 2019

The meeting was called to order by Committee Chair Jill O’Donnell-Tormey at 3:00 p.m.

The following people were present:

Committee Members: Trustee Observer: Hon. Jill O’Donnell-Tormey, Chair Hon. Haris Khan Hon. Charles A. Shorter, Vice Chair Hon. Henry T. Berger Trustee Staff: Deputy Secretary Anne Fenton Faculty Member: Assistant Secretary Towanda Lewis Prof. Martin Burke, faculty representative University Staff: Student Member: Interim Chancellor Vita C. Rabinowitz Mr. Farbod Moghadam, student representative Deputy General Counsel Jane Sovern

COP Liaison: President William J. Fritz

Cal. No. DISPOSITION

The agenda items were considered and acted upon in the following order:

I. ACTION ITEMS:

A. APPROVAL OF THE MINUTES OF THE MEETING OF JANUARY 14, 2019. Moved by Prof. Martin Burke and seconded by Trustee Henry Berger, the minutes were unanimously approved as submitted.

B. POLICY CALENDAR

1. Queens College - BFA in Photography and Imaging. Interim Chancellor Vita Rabinowitz stated that advances in technology have revolutionized photography and imaging. There are growing employment opportunities for people with photo editing and researching, commercial, industrial and scientific photography, 2D and 3D imaging and digital archiving. Queens College has a thriving Art Department with more than 500 majors including several strong programs in art, art history and media, but currently only offers photography as a concentration. This BFA in Photography and Imaging will compliment arts education at Queens College, and provide graduates with excellent professional skills at one-third the price of similar programs at private schools in NYC. Most of the courses in this proposed program already exist as part of other degrees and only one new faculty member will be hired to launch the program in addition to possibly five adjunct faculty. In addition, Queens College has been awarded a $500,000 capital improvement grant from the Queens Borough President and the New York City Council to fund laboratory renovations and equipment to support an enhanced photography curriculum.

A discussion about the proposal followed, including academic excellence fees, articulation agreement, transferability of credits, and faculty funding.

Moved by Trustee Berger and seconded by Prof. Burke, and following discussion, the item was unanimously approved for submission to the Board.

BOARD OF TRUSTEES THE CITY UNIVERSITY OF NEW YORK

COMMITTEE ON MINUTES OF THE MEETING ACADEMIC POLICY, PROGRAMS AND RESEARCH FEBRUARY 25, 2019

2. Queens College - Closing of the Department of Student Personnel. Interim Chancellor Rabinowitz stated that at Queens College, as elsewhere at CUNY, student services historically provided in academic department staffed by faculty are now offered via administrative divisions, staffed by full-time professionals year-round. This allows for more robust services for students, and greater flexibility in service delivery. The prior academic department structure had been retained for administrative purposes, but now is being dissolved, because there is only a single faculty member still remaining in the department. The remaining faculty member is being transferred to the Department of Education and Community Programs via a resolution by the Committee on Faculty, Staff and Administration, in tandem with this resolution to close the department. She further reiterated that the services the department provided are now and will continue to be provided through the Division of Student Affairs in a more robust and diversified manner.

Moved by Trustee Berger and seconded by Committee Vice Chair Charles Shorter, and following discussion, the item was unanimously approved for submission to the Board.

3. New York City College of Technology - AS in Health Science. Interim Chancellor Rabinowitz stated that New York City College of Technology currently offers more than a third of all CUNY undergraduate technology degrees. Many of those degrees sit at the intersection of health and technology, one of the most robust sectors in New York City’s economy. Presently, the college does not offer a coordinated academic experience in which students can explore tech and health-related interests. This flexible associate’s degree will expose students to a wide range of career opportunities as both the licensed professions in the allied health care sector as well as the managerial and technical arenas of running health care organizations, including Biomedical Information, Applied Chemistry, and Technology Teacher Education. This degree will also prepare them for transfer to a senior college. An articulation agreement already exists with SPS, one of CUNY’s fastest growing colleges and one that is also at the nexus of health and tech careers. The College expects that this flexible program will improve the retention rates of students interested in health and other STEM-related disciplines.

President Russell Hotzler provided additional information about the AS program in Health Science.

A discussion about the proposed program followed, including the degree selection, expenses, and articulation agreements.

Prof. Martin Burke praised the program proposal.

Moved by Committee Vice Chair Shorter and seconded by Trustee Berger, and following discussion, the item was unanimously approved for submission to the Board.

4. Hunter College - BA in Arabic. Interim Chancellor Rabinowitz stated that Arabic had been identified by the US State Department as a critical language for study in the US because of its growing relevance to American foreign policy, national security and commerce. There is currently no Arabic major in NYC—not at CUNY, not at any private college. Hunter College proposes to build upon its strong Arabic program and existing faculty expertise to build NYC’s only Arabic major. The Arabic major will provide language fluency and cultural competency to heritage- and non-heritage-Arabic-speaking students alike, offering a wide range of opportunities for employment and graduate study. Hunter

BOARD OF TRUSTEES THE CITY UNIVERSITY OF NEW YORK

COMMITTEE ON MINUTES OF THE MEETING ACADEMIC POLICY, PROGRAMS AND RESEARCH FEBRUARY 25, 2019

College has received expressions of support for the major in Arabic from within and beyond the College, and has provided examples of employment opportunities in this area. The College has long had one of the strongest language programs at CUNY, and currently has the largest and most comprehensive Arabic program at CUNY. Thus, the proposed major is right for Hunter College and provides a unique opportunity for CUNY students.

Moved by Committee Vice Chair Shorter and seconded by Trustee Berger, and following discussion, the item was unanimously approved for submission to the Board.

5. CUNY Office of Academic Affairs - Approval of the Academic Board Report. Interim Chancellor Rabinowitz stated that items in this report consist primarily of actions required to be forwarded to the New York State Education Department for review, as well as additions to the University’s general education program and local academic policies.

Prof. Burke inquired about where to find the detail Academic Board Report (ABR) during the transitional period thus the new processes. Interim Chancellor Rabinowitz responded that changes in policy have slightly out-paced the creation of a supporting infrastructure for the new practices; however, going forward, the detail report will be posted on the Office of Academic Affairs (OAA) website prior to each Committee on Academic Policy, Programs and Research meeting. She further noted that no changes will occur with respect to the governance approvals and/or general oversight.

Committee Chair O’Donnell-Tormey concluded by stating the new practices are all an aim to being more transparent.

Moved by Committee Vice Chair Shorter and seconded by Trustee Berger, and following discussion, the item was unanimously approved for submission to the Board.

Mr. Farbod Moghadam moved to adjourned the meeting. The motion was seconded by Trustee Henry Berger and the meeting was adjourned at 3:21 p.m.

I.B.1 – NEW YORK CITY COLLEGE of TECHNOLOGY– BS in DATA SCIENCE

WHEREAS, Data Science is one of the fastest growing fields and continues to gain momentum, as estimated by industry experts, and

WHEREAS, the proposed program will be a synthesis of applied mathematics, high- performance computing, and data management and analysis, all skills that are in high demand by employers, and

WHEREAS, students will gain a quantitative understanding of logical, statistical and business analysis as well as computing principles, and

WHEREAS, the program will prepare students for data science jobs in a variety of applied fields as well as prepare them for further study in the Graduate School’s MS in Data Science program, and

WHEREAS, the proposal has received enthusiastic letters of support from the CUNY Graduate Center’s Master’s program in Data Science and their PhD program in Computer Science as well as strong letters of support from the industry, and

WHEREAS, students with an Associate Degree in Computer Science, Computer Information Science and Mathematics from across the University will be candidates for admission into the new program, and

WHEREAS, an articulation agreement has been signed with the Borough of Manhattan College, and articulation agreements with other CUNY community colleges are in process, and

WHEREAS, this innovative program is consistent with New York City College of Technology’s strategic goals and builds on existing faculty strengths in the Computer Systems Technology Department, so be it

RESOLVED, that the program in Data Science at New York City College of Technology, leading to the Bachelor of Science, be approved effective May 8, 2019, subject to financial ability.

EXPLANATION: As New York City College of Technology currently offers an AAS in Computer Information Systems and a B.Tech in Computer Systems, the proposed program is a logical further expansion into a related field with high employment growth. The curriculum builds on existing database and programming language courses while adding eight new courses on the core areas of data science. This program will provide students with strong technical skills, critical thinking, and problem-solving abilities that are highly rated by companies in many fields including, but not limited to, Finance, Programming, Education, Medicine and Biology, providing pathways for employment after graduation as well as for admission into graduate programs. This will be the first such program at the undergraduate level at CUNY. No additional fees are being proposed.

NEW YORK CITY COLLEGE OF TECHNOLOGY OF THE CITY UNIVERSITY OF NEW YORK

PROPOSAL TO ESTABLISH A PROGRAM IN DATA SCIENCE LEADING TO THE BACHELOR OF SCIENCE DEGREE

EFFECTIVE FALL 2019

SPONSORED BY THE DEPARTMENT OF COMPUTER SYSTEMS TECHNOLOGY

APPROVED BY NEW YORK CITY COLLEGE OF TECHNOLOGY COLLEGE COUNCIL, December 4, 2018

College Representative: Bonne August, Provost and VP of Academic Affairs Contact: Telephone: (718) 260-5560 Fax: (718) 260-5542 Email: [email protected]

Provost’s Signature: Provost’s Name: ______Bonne August______

TABLE OF CONTENTS Executive Summary ...... 12 Abstract ...... 14 1. Purpose and Goals ...... 15 2. Need and Justification ...... 16 2.1 Employment Opportunities After Graduation ...... 16 2.1.1 Overview of job market ...... 16 2.1.2 Job market growth...... 17 2.1.3 Compensation Potential ...... 17 2.2 Related Undergraduate and Graduate Programs in the New York area (as of November 5, 2018)...... 18 2.2.1 Related Programs in CUNY ...... 18 2.2.2 Related Programs in SUNY ...... 19 3. Student Interest and Anticipated Enrollment...... 20 3.1 Admissions Requirements ...... 22 3.1.1 Students entering the Data Science program with no prior higher education experience: ...... 22 3.1.2 Students transferring from other colleges: ...... 22 4. Overview of the Courses in the Curriculum ...... 23 4.1 Overview of the Courses in the Curriculum...... 23 4.2 Anticipated Learning Outcomes...... 24 4.3 Courses Required to Complete the Program ...... 25 4.4 Mapping Anticipated Learning Outcomes to the Courses ...... 30 4.5 Evaluation...... 31 5. Cost Assessment ...... 32 5.1 Faculty and Staff ...... 32 5.2 Facilities ...... 33 5.3 Library ...... 33 References ...... 34 Appendix A: Course Descriptions For Existing Required Courses ...... 35 Appendix B: New Courses...... 38 B.1 Descriptions of New Courses ...... 38 B.2 Syllabi of New Courses ...... 41

B.2.1: CST2312 Information and Data Management I ...... 41 B.2.2: CST2402 Introduction to Data Science ...... 46 B.2.3: CST3502 Data Mining ...... 51 B.2.4: CST3512 Information and Data Management II ...... 56 B.2.5: CST3602 Data Visualization ...... 61 B.2.6: CST4702 Machine Learning Fundamentals ...... 65 B.2.7: CST4802 Information Retrieval ...... 70 B.2.8: CST4812 Natural Language Processing ...... 75 Appendix C: Letters of Support ...... 78 C.1: Letters of support from CUNY community colleges ...... 78 C.2: Letters of support from graduate programs in the NYC area ...... 80 C.3: Letters of support from industry ...... 81 Appendix D: Articulation Agreement with Community Colleges ...... 83 Appendix E: Employment Status and Sample Employment Opportunities ...... 88 Appendix F: General Information ...... 101 Appendix G: Program Purpose, Objectives and Targets ...... 103 Appendix H: Curriculum and Course Information ...... 104 Appendix I: Undergraduate Program Schedule ...... 105 Appendix J: Faculty Teaching Assignments Form ...... 107 Appendix K: Faculty to be Hired ...... 111 Appendix L: New Resources, Projected Revenue, and Financial Projections ...... 112

Executive Summary

Data Science is “the study of data” combining statistics, informatics and computing in an effort to understand all aspects of data including environment, organization and the social and ethical implications. The results of such a data driven understanding is knowledge discovery leading to intelligent decision making (Cao, 2017). Students enrolled in the Bachelor of Science (BS) in Data Science program will gain a quantitative understanding of logical, statistical and business analysis as well as computing principles. They will be versed in various computing methods and tools used to analyze and understand massive amounts of data. Graduates of the program can benefit from the abundance of employment opportunities available both in public and private sectors within the tristate area and beyond. In addition, graduates of the program can pursue graduate studies at several graduate schools including the CUNY Graduate Center. The Computer Systems Technology Department (CST) at New York City College of Technology proposes a Bachelor of Science degree program in Data Science. This program would be a synthesis of applied mathematics, high-performance computing, data management and analysis. The BS in Data Science program will provide students with strong technical skills, critical thinking, and problem-solving abilities that are highly rated by companies in all fields, including but not limited to, Finance, Programming, Education, Medicine, Biology. Graduates from this program will be well equipped with a solid platform in mathematics, computing, and data management and analysis, and the program will provide pathways for employment after graduation, as well as for admission to graduate programs, including CUNY own MS in Data Science program in CUNY Graduate Center. At the same time, it aligns with the recent efforts in several CUNY colleges, as well as colleges outside the CUNY system, to provide students with the skills and knowledge which are currently in high demand. The proposed BS in Data Science is consistent with the mission of New York City College of Technology in that it will provide students with the educational foundation as well as the command of the technical skills necessary to succeed in the domains where Data Science is applied. The degree will offer a balance of technical and liberal arts courses in an effort to foster intellectual curiosity, an appreciation for the aesthetic dimension of life and work and a respect for cultural diversity. The BS in Data Science is also consistent with the mission of the CST Department as courses offered will emphasize both the theoretical and practical foundation in the Data Science domain and will emphasize a “hands- on” approach for maximum learning and retention of concepts and practices.

The curriculum is designed to provide general education, solid foundation of computer system, mathematical algorithm and statistical skills to advance the field of study, core data science including Data Management, Data Visualization, Natural Language Processing, Machine Learning and Information Retrieval. In addition, electives allow students to explore application of data science techniques and methods in a variety of domains such as medicine, biology, engineering, finance and business, etc. The CST department currently offers AAS degree in Computer Information System and B.Tech degree in Computer Systems which includes four concentration: Database, Networking and Security, IT Operations and Software development. In addition to some existing database and programming language courses, the proposed BS in Data Science program includes eight new courses introduce core areas of data science. The CST department has twenty tenured and tenure- track faculty members qualified to teach all the classes related to the proposed program. Six faculty members are active in research fields involving data analysis, data mining, and machine learning. Their research includes the use and development of computational methods for data analysis with application to bioinformatics, text mining, etc. In addition, there are several faculty members in other Departments within CityTech who can provide support and bring their expertise to the proposed curriculum, in particular in the Mathematics, Physic, Biology, and Social Science Departments.

Abstract

The Computer Systems Technology Department at New York City College of Technology proposes a Bachelor of Science degree program in Data Science. This program would be a synthesis of applied mathematics, high-performance computing, data management and analysis, which is in high demand, and does not currently exist at any of the CUNY colleges. The BS in Data Science program will provide students with strong technical skills, critical thinking, and problem-solving abilities that are highly rated by companies in all fields, including but not limited to, Finance, Programming, Education, Medicine, Biology. Our graduates will be well equipped with a solid platform in mathematics, computing, and data management and analysis, and the program will provide pathways for employment after graduation, as well as for admission to graduate programs, including CUNY own MS in Data Science program in CUNY Graduate Center.

1. Purpose and Goals

The purpose and goal of the BS in Data Science program is to prepare graduates with the technical skills necessary to enter careers in the Data Science field, which is one of the fastest- growing fields today. According to Forbes magazine and industry experts (Kobielus 2016 and Sentz 2016) data scientists are in high demand and will continue to be in high demand as the big data market continues to gain momentum. Experts state (Kobielus, 2016) that high priority enterprise application projects require skilled data scientists who have knowledge in machine learning and cognitive computing. These key topics in addition to other integral topics in the field of data science are studied as part of the BS in Data Science program. Graduates of the BS in Data Science will acquire the conceptual and computational understanding as well as the technical expertise needed to excel in this field. In addition, they will also gain the real-world experience necessary to meet the industry need for skilled Data Scientists. The BS in Data Science curriculum focuses on the knowledge and skills required to meet industry’s data challenges. Students will also complete the CUNY Pathway general education requirements which provide students with a solid liberal arts education. Students will also be required to complete several courses in mathematics including probability, discrete structures, linear algebra and calculus. Students will take two foundational courses in an area of their choosing such as business, economics, marketing, biomedical, and healthcare providing them with an understanding of the role data science plays within their chosen area. The courses in the major will consist of key computational topics such as natural language processing, machine learning, data mining, data retrieval and visualization as well as mathematical and analytical topics which will prepare students to meet the data challenges of the field. A combination of lecture, hands-on labs, group work and an internship will provide students with the opportunity to learn and become skilled data scientists who can then apply those skills on the job. In the first two years of the program students become well versed in the foundational principles of computer systems and data science topics. In the last two years, courses are designed to cover the breadth and depth of the data science field. Consistent with City Tech’s educational goals, the BS in Data Science is designed to provide a well-rounded interdisciplinary education for the new generation of Data Scientists who will be prepared to meet the challenges of the domain. Graduates of the program should be able to secure

employment as data scientists in various domains including financial, business, marketing and medical as well as others. In addition, graduates should be able to make positive contributions to the field.

2. Need and Justification

The BS in Data Science program has been designed so graduates of the program can: (1) benefit from the excellent employment opportunities in the public and private sectors in the tristate area and beyond; (2) pursue graduate studies. This new program will significantly increase the opportunities available to City Tech and CUNY students transferring to City Tech for employment or graduate education and who are interested in the application of data science techniques and methods to a variety of domains, such as, finance, biology, medicine, business, etc. 2.1 Employment Opportunities After Graduation

2.1.1 Overview of job market

According to data produced by Emsi (http://www.economicmodeling.com) and CareerBuilder (https://hiring.careerbuilder.com), there were, on average, 2,900 unique job postings active per month for data scientists over the past nine months. (NOTE: The total number of job postings is, of course, much higher because employers advertise on many different job sites. Emsi has de- duplicated those postings to the real number of locations / businesses posting ads for those jobs.) The top states for data science job postings are California, Washington, New York, Virginia, and Massachusetts, while the top metros are San Jose, Seattle, New York, Washington D.C., Chicago, and San Francisco. 59% of all Data Science and Analytics job demand is in Finance and Insurance, Professional Services, and IT. Companies looking for this talent represent virtually every industry. Oracle, Microsoft, Amazon, Apple, Booz Allen Hamilton, GE, State Farm, Walmart, Facebook, United Health Care, Aetna, AT&T, Intel, IBM, Nielsen, KPMG, eBay and many more all show up prominently in job postings for data scientists. Related job titles include: • Data analyst • Data scientist • Data mining engineer • Machine learning engineer

• Data architect • Hadoop engineer • Data warehouse architect • Commercial intelligence manager • Competitive intelligence analyst • Consultant, strategic business and technology intelligence

2.1.2 Job market growth

Forbes predicts that annual demand for the fast-growing new roles of data scientist, data developers, and data engineers will reach nearly 700,000 openings by 2020. By 2020, the number of jobs for all US data professionals will increase by 364,000 openings to 2,720,000 according to IBM. Data Science and Analytics jobs remain open an average of 45 days, five days longer than the market average indicating difficulty in finding qualified talent (Career, 2017).

2.1.3 Compensation Potential

According to Forbes (Forbes, 2016) (Forbes, 2017), jobs requiring machine learning skills are paying an average of $114,000. Advertised data scientist jobs pay an average of $105,000 and advertised data engineering jobs pay an average of $117,000. According to Glassdoor, the annual median salary for a data scientist is $121,353. In his article, Big Data Salaries: An Inside Look, DataJobs.com founder Frank Lo conducted extensive research to identify the following job titles and salary ranges (Lo, 2017): • Data analyst—entry level | Annual salary: $50,000–$75,000 • Data analyst—experienced | Annual salary: $65,000–$110,000 • Data science/analytics manager—1 to 3 direct reports | Annual salary: $90,000–$140,000 • Big data engineer—junior/generalist | Annual salary: $70,000–$115,000 • Big data engineer—domain expert | Annual salary: $100,000–$165,000 According to O’Reilly’s 2016 Data Science Salary Survey (O'Reilly, 2016), experience is one of the most important factors in a data scientist’s salary. For every year of experience, data science professionals make an average of $2,000 to $2,500 more. A 2017 Burtch Works study (Burtch, 2017) of data science salaries reported the latest salary trends based on experience:

• Entry-level data scientist salary. Despite a recent influx of early-career professionals, the median starting salary for a data scientist remains high at $95,000. • Mid-level data scientist salary. The median salary for a mid-level data scientist is $126,000. If this data scientist is also in a managerial role, the median salary rises to $190,000. • Experienced data scientist salary. The median salary for experienced data science professionals is $157,000—while the median salary for experienced manager-level professionals is considerably higher at $250,000.

2.2 Related Undergraduate and Graduate Programs in the New York area (as of November 5, 2018). 2.2.1 Related Programs in CUNY

Undergraduate Programs Three colleges within CUNY offer exposure to undergraduate students in the area of Data Analytics and Data Science. • Baruch College, offers a Data Analytics track as part of their Computing in Information Systems (CIS) Major - Bachelor of Business Administration (BBA) Degree; as well as Data Analytics Minor (as one of IS Business Minors) for the Baruch Weissman School or Arts and Sciences or in the School of Public Affairs. The Baruch programs are designed to provide the background on “[…] how to aggregate, analyze, and monetize the growing surge of available data. The new track – Data Science and Analytics – would provide a strong foundation in technology, statistics, and quantitative modeling that is needed to develop business intelligence and drive organizational decision-making.” The CSI programs focus on Data Science methods and techniques application to the fields of Business Management and Intelligence, Econometrics, and Marketing. • John Jay College of Criminal Justice offers a BS degree in Applied Mathematics with concentration in Data Science. The John Jay Data Science concentration “presents the principles of data representation, big data management, and statistical modeling.” Master’s Programs At the Master’s level, as of November 5, 2018, four schools offer related degrees: • City College of New York offers an MS program in Data Science and Engineering.

• CUNY Graduate Center offers an MS program in Data Science with topics including artificial intelligence, algorithms for big data, data visualization, information retrieval, data mining, and machine learning. • CUNY School of professional studies offers an online degree program which provides foundational knowledge and hands-on programming competencies, resulting in project- based work samples. • Queens College, a joint program between the Computer Science department and the department of Sociology offers and MS in Data Science.

2.2.2 Related Programs in SUNY

Three universities within SUNY offer Master’s degrees in the area of data analytics. • SUNY Albany offers a new program in data science in their Mathematics and Statistics Department. • SUNY Buffalo offers a Master’s of Science in Engineering Sciences with a focus on Data Sciences. This program provides students with a core foundation in big data and analysis by obtaining knowledge, expertise, and training in data collection and management, data analytics, scalable data-driven discovery, and fundamental concepts. • SUNY Syracuse offers an online Master’s program in Data Science.

3. Student Interest and Anticipated Enrollment

Student Interest: According to the eFinancialCareers.com (a targeted career resource and employment website from the Wall Street Journal) “Most industries are struggling to find data science expertise, but Wall Street especially is particularly keen to hire in this area.” A majority of City Tech students live in the New York City tristate area and are not only aware of the high demand for data science skills, but are also very interested in gaining these skills in order to secure careers in the Data Science field which includes not only the financial industry but other industries such as Medical, Biology and Hospitality. In addition, many City Tech students are interested in continuing their education in the field of Data Science. Several universities in the New York City and its vicinities offer Master of Science degrees in Data Science. However, only a few universities offer Bachelor level degrees in Data Science related areas. The BS in Data Science degree proposed here will increase the opportunity for students in and around New York City. A survey of 198 City Tech CST students (both AAS and BTech) was conducted in the Spring of 2017 to identify interest and demand among City Tech students. Students participating in the survey were offered a brief description of a tentative Data Science program at City Tech. The following is a sample of the survey questions and the results obtained from student responses:

1. How familiar are you with the terms big data, data analysis, data science?

This is the first I have heard these I am familiar with I have a good time I see these terms but do not these terms and understanding of Total terms. have a good grasp have a general these terms of what they understanding of mean. what they mean. 11(5.56%) 77(38.89%) 85(42.93%) 25(12.63) 198 (100%)

2. Based on the description of the Bachelor of Science Degree in Data Science, would you be interested in pursuing this Bachelor of Science degree at City Tech? Answer Choice Responses Yes 139 (70.20%) No 59 (29.80%) Total 198 (100%)

Our preliminary conclusions are: 1) many City Tech CST students have an understanding of the various aspects of the Data Science field and, 2) students are interested in pursuing a BS degree in Data Science at City Tech. Enrollment Outlook: We can estimate enrollment outlook for the next five years based on the rate of growth of the CST student population for the last five years (2012-2016). This estimate is partly based on data acquired from Enrollment Trends found in the Assessment and Institutional Research (AIR) website (http://air.citytech.cuny.edu/data-dashboard/enrollment-trends-fall), and calculations of the average rate of growth over the five year period 2012-2016. These calculations show a 19% average growth for new incoming CST freshmen students and a 7% average growth for transfer students coming into the CST department. Based on these average growth rates and the survey results indicating a 75% interest in the BS in Data Science, we can estimate the following growth over the next five-year period 2019- 2023:

Year No. of Estimated no. No. of Transfer Estimated no. of CST of students in Students transfer students in Students BS/DS BS/DS

2019 409 76 219 41

2020 437 82 234 43

2021 467 87 250 46

2022 499 93 267 50

2023 499 93 267 50

For incoming new freshmen CST students, the rate of growth for this population in the last five years was 19%. For transfer students coming into the CST department, the rate of growth is 7%. Potential Students: City Tech students in the Bachelor of Technology (BTech) curriculum with an interest in Data Science and who meet the requirements of the new program will be potential candidates for the Bachelor of Science in Data Science. In addition, students with Associate Degrees in Computer Science, Computer Information Science and Mathematics from any of the CUNY community colleges including Borough of Manhattan, La Guardia, Kingsborough, Bronx Community College, Lehman College, York College, Medgar Evers College and the College of Staten Island would also be potential candidates for this program.

3.1 Admissions Requirements 3.1.1 Students entering the Data Science program with no prior higher education experience:

New students should have graduated from high school with an overall average of 75.0 or higher and be fully CUNY certified in reading, writing and math and be academically qualified to register for ENG 1101 (English Composition I). High school coursework in Mathematics will be helpful but is not required. Students who do not have the mathematics background required to enroll in MAT 1475 (Calculus I) but who meet other admission requirements can use the program elective credits to complete the pre-requisite courses.

3.1.2 Students transferring from other colleges:

Students transferring to the Data Science program should have an overall GPA of 2.5 or higher, and have completed at least one semester of college-level English composition and one semester of Calculus (i.e. a course equivalent to MAT 1475), or have the approval of the CST Department Chair. College coursework in Programming and Data Analysis will be useful for applicants but is not required for admission.

4. Overview of the Courses in the Curriculum

4.1 Overview of the Courses in the Curriculum

The Bachelor of Science in Data Science curriculum includes: 1. The CUNY Pathways general education requirements (required core, flexible core, college option) which will help students develop a solid liberal arts education (42 credits). 2. Required general education courses in mathematics which will help students develop a strong foundation in the mathematics and statistics necessary to succeed in subsequent Data Science courses (18 credits). 3. Computer systems core courses taken mostly during the first two years of the program (24 credits) will introduce students to the fundamental concepts of Computer Systems (Databases, Problem Solving, Programming Fundamentals, Introduction to Security and Web Programming) and Data Science (Information and Data Management I, and Introduction to Data Science). 4. Data Science core with advanced topics are taken during the last two years of the program (27 credits). The program designed to balance breadth and depth in Data Science, offers different topics in the field of Data Science including: Data Mining, Information and Data Management II, Information Retrieval, Natural Language Processing, Machine Learning Fundamentals and Data Visualization. 5. Data Science Major Electives (6 credits). Those courses are designed so that students can take two major electives in a practical field of data science application (e.g. Finance, Healthcare, Economics and Biomedical Engineering, Internet of Things and Artificial Intelligence). 6. Internship (3 credits). Students are exposed to the practice of the profession in research or industry.

A grade of “C” or better in each course designated with the prefix CST will be required for progression towards graduation.

4.2 Anticipated Learning Outcomes The curriculum has been designed to meet the following learning outcomes: Student Learning Outcomes: General Students demonstrate: a. An ability to apply the knowledge, techniques, skills, and modern tools of the discipline to Data Science activities. b. An ability to apply a knowledge of mathematics, science, engineering, and technology to Data Science problems that require application of principles and practical knowledge. c. An ability to conduct standard tests and measurements, and to conduct, analyze, and interpret experiments. d. An ability to function effectively as a member of a technical team. e. An ability to apply written, oral, and graphical communication in both technical and non- technical environments; and an ability to identify and use appropriate technical literature. f. Demonstrate an understanding of the need for and an ability to engage in self-directed continuing professional development. g. Demonstrate an understanding of and a commitment to address professional and ethical responsibilities, including a respect for diversity. h. A commitment to quality, timeliness, and continuous improvement in professional practice. Student Learning Outcomes: Discipline-Specific Data Science students demonstrate knowledge and hands on competence in: a. Analyzing, designing, and implementing data science algorithms along while performing analytics. b. Demonstrating a deep knowledge of: Data Mining, Data Management, Data Analytics, Information Retrieval; enabling students to gain employment in the data science field. c. Demonstrate an understanding of how management uses data science systems to operate business enterprises. d. Demonstrate a deep understanding of techniques for visualizing multivariate, temporal, text-based, geospatial, hierarchical, and network/graph-based data. e. Demonstrate an understanding of the ethics and privacy issues that arise from the process of managing and mining user data.

4.3 Courses Required to Complete the Program

The proposed curriculum for the Bachelor of Science in Data Science is detailed below. The curriculum reflects City Tech’s General Education requirements:

General Education Common Core 42 credits Required General Education 18 credits Computer Systems Fundamentals 24 credits Data Science Core 27 credits Data Science Major Electives 6 credits Internship 3 credits TOTAL 120 credits

General Education Core: Required Core:

English Comp1 ENG1101 3 English Comp 2 ENG1121 3 Quantitative Reasoning Any 3-4 Life & Physical Science Any 3-4

Flexible Core:

World Culture and Global Issues Any 3 US Experience and Diversity Any 3 Creative Expression Any 3 Individual and Society Any 3 Scientific World Any 3 Additional 6th course Any 3

College Option:

Speech/Oral Communication Any 3 Interdisciplinary Course Any 3 Additional LA I Any 3 Additional LA II Any 3

Sub-Total 42-44

Program General Education Required:

Calculus I MAT1475 4 Calculus II MAT1575 4 Probability and Statistics I MAT2572 4 Discrete Structures and Algorithms I MAT2440 3 Introduction to Linear Algebra MAT2580 3

Sub-Total 18

BS major core requirements:

Computer Systems Fundamentals: Introduction to Computer Systems CST1100 3 Problem Solving with Computer Programming CST1101 3 Programming Fundamentals CST1201 3 Database Fundamentals CST1204 3 Information and Data Management I CST2312 3 Web Programming I CST2309 3 Introduction to Data Science CST2402 3 Introduction to Security CST2410 3

Sub-Total 24

Data Science Core: Information and Data Management II CST3512 3 Data Mining CST3502 3 Object Oriented Programming CST3513 3 Data Visualization CST3602 3 Data Structures CST3650 3 Machine Learning Fundamentals CST4702 3 Database Administration CST4714 3 Information Retrieval CST4802 3 Natural Language Processing CST4812 3

Sub-Total 27

Internship CST4900 3

Sub-Total 3

Data Science major electives (Note: Take any course(s) to complete 120 total credits and 60 credits of Liberal Arts)

Financial management BUS2339 3 Financial Forecasting BUS2341 3

Macroeconomics ECON1101 3 Money and Banking ECON2301 3

Medical Informatics Fundamentals MED2400 3 Healthcare Databases MED4229 3

Fundamental Healthcare Data Analytics BMET4741 3 Advanced Healthcare Data Analytics BMET4842 3

Internet of Things CET4925 3 Introduction to Artificial Intelligence CET4973 3

Probability and Statistics II MAT3672 3 Computational Statistics with Applications MAT4672 4

*Machine Learning for Physics and Astronomy PHYS3600 3

Total Credits 120

*Specific course indicates double duty course, i.e. program degree requirement that also meet general education requirements. Choose another elective to complete 120 credits if choosing to take double duty.

Example of a Four Years Course Sequence FIRST YEAR First Semester CST 1100 Introduction to Computer Systems 3 CST 1101 Problem Solving 3 Required Core/Quantitative Res. Any 3 Required Core/ENG 1101 English Composition I 3 Flexible Core/World Cultures Any 3 TOTAL 15

Second Semester CST 1201 Programming Fundamentals 3 CST 1204 Database Fundamentals 3 MAT 1475 Calculus I 4 Required Core/Life &Physical Sc. Any 3 Required Core/ENG 1121 English Composition II 3 TOTAL 16

SECOND YEAR First Semester CST 2312 Information and Data Management I 3 CST 2309 Web Programming 3 MAT 1575 Calculus II 4 MAT 2440 Discrete Structures and Algorithms I 3 Flexible Core/Any Any 3 TOTAL 16

Second Semester CST 2402 Intro to Data Science 3 CST 2410 Intro to Computer Security 3 MAT 2572 Probability and Statistics I 4 Flexible Core/US Experience Any 3 TOTAL 13

THIRD YEAR First Semester CST 3512 Information and Data Management II 3 CST 3513 Object Oriented Programming 3 CST 3502 Data Mining 3 Flexible Core/Scientific World Any 3 College Option/LLAA I Any 3 TOTAL 15

Second Semester CST 3602 Data Visualization 3 CST 3650 Data Structures 3 MAT 2580 Intro to Linear Algebra 3 Flexible Core/Creative Expression Any 3 College Option/LLAA II Any 3 TOTAL 15

FOURTH YEAR First Semester Data Science Major Elective I Any 3 CST 4702 Machine Learning Fundamentals 3 CST 4714 Database Administration 3 College Option/Speech Any 3 Flexible Core/Individual & Society Any 3 TOTAL 15

Second Semester Data Science Major Elective II Any 3 CST 4802 Information Retrieval 3 CST 4812 Natural Language Processing 3 CST 4900 Internship 3 College Option/Interdisciplinary Any 3 TOTAL 15

4.4 Mapping Anticipated Learning Outcomes to the Courses

The following table maps CST courses to student outcomes a-k (see above: section 4.2 Anticipated Student Outcomes):

Courses a b c d e f g h i j k

CST 1100: Introduction to Computer X X X X X X X X X Systems CST 1101: Problem Solving X X X X X X X

CST 1201: Programming Fundamentals X X X X X X X

CST 1204: Database Fundamentals X X X X X X X CST 2312: Information and Data X X X X X X X Management I

CST 2309: Web Programming I X X X X

CST 2402: Introduction to Data Science X X X X X

CST 2410: Introduction to Security X X X X X X X CST 3502: Data Mining X X X X X X X X X X X

CST 3512: Information and Data X X X X X Management II CST 3513: Object Oriented X X X X X Programming CST 3602: Data Visualization X X X X

CST 3650: Data Structures X X X X X X X X

CST 4702: Machine Learning X X X X X X X X Fundamentals CST 4714: Database Administration X X X X X X

CST 4802: Information Retrieval X X X X X X

CST 4812: Natural Language Processing X X X X X X X X X X X

CST 4900: Internship X X X

4.5 Evaluation

The BS in Data Science program will be evaluated using a variety of instruments. As a program, in addition to regular review per CUNY’s Academic Program Review Policy, it will be included in the assessment cycles of all City Tech programs. These assessments include General Education, Critical Course, Program level outcomes and First Year Experience. As mentioned in the proposal one purpose of the program is to provide students with information about the multiple career paths available to them via this degree, and then provide them with the flexibility to make an informed choice. The pool of students will include those who want to benefit from the abundance of employment opportunities available both in public and private sectors in Data Science, and those who can pursue graduate studies at several graduate schools including the CUNY Graduate Center Master’s program in Data Science.

The specific evaluation criteria will include progression and completion rates, and success of students in the Master’s degree program into which they articulate. We will also investigate students who desire career entry after the baccalaureate degree, and track their employment status.

Participants in the evaluation of the degree include all program faculty members, as well as an internal advisory group consisting of representatives from industry, who will provide liaison support and evaluate efficacy of students entering their companies via the BS in Data Science degree. In addition, the Director of Institutional Assessment, Dean, Associate Provost, and Assessment Liaisons will participate.

5. Cost Assessment

Current classroom equipment is sufficient for instruction delivery of all courses in the proposal. Other than our regular maintenance and replacement of current existing equipment, no additional equipment will be required. However, the CST department currently has 20 full time faculty members and 90 adjunct faculty members providing academic support of more than two thousand students. We anticipate the total number of students will increase with the new proposed degree program. The department currently is recruiting for two positions of tenured track Assistant Professor. 5.1 Faculty and Staff The Computer Systems Technology (CST) Department currently has 8 tenured or tenure-track faculty qualified to teach all or a portion of the coursework related to the proposed B.S. degree program. We anticipate hiring two faculty members for 2018-2019 to handle the increased number of students taking CST classes as well as to replace faculty who have resigned or assumed administrative duties. We currently have faculty members with the necessary expertise to develop the new courses in the proposed curriculum with an emphasis on data science. In particular, many of the faculty members are active in research involving machine learning, data mining, and data analysis in various fields (including but not limited to biology, natural language processing, finances). Below are some faculty members and their research interests: o Candido Cabo: computational biology and bioinformatics, high performance computing and computer science education. o Yu-Wen Chen: cloud computing, smart grid, information security, and big data optimization. o Elena Filatova: text mining, natural language processing. o Hong Li: mathematical modeling and artificial neural networks o Elizabeth Milonas: relational and non-relational database modeling and programming, information retrieval

o Ashwin Satyanarayana: building Data Mining tools and Big Data analytics.

In addition, there are several faculty members in other Departments that can provide support and bring their expertise to the proposed curriculum, in particular in the Mathematics department.

5.2 Facilities The CST network under college network is designed inclusively for CST classes. Currently 14 classrooms all equipped with 24 computers and one open lab provides outside class hands-on experiences. All computers have access to the CST servers which house database servers, and other application, etc. In addition, students can access applications throughout the college through the CST servers. The recently purchased cluster had capacity to house VM for various class applications. A new lab space is designated to the CST department and it is under renovation. A state-of-art lab will be set up to enhance hands-on delivery and exercises.

5.3 Library The Ursula C. Schwerin Library maintains a faculty specialist in the area of computer systems (Dr. Junior Tidel), whose job is dedicated to the needs of the CST department. The library also maintains an ample physical and online collection of resources that can be and will be utilized by the Data Science program. The college and university wholly fund the library resources, and no additional costs are anticipated.

We believe that the library has ample resources, which will benefit the program, both in physical and electronic format. However, the library might consider the acquisition of textbooks and documentation related to the Data Science courses. All CST Department faculty work in concert with the library liaison, Dr. Tidel, to ensure library resources are both relevant and beneficial as well easily available to CST students The department actively participates in the Open Education Resource (OER) program coordinated by the Ursula C. Schwerin Library specialist Dr. Cailean Cooney. The goal of this program is to provide City Tech students with no-cost resources such as textbooks and other course materials.

References

Burtch. (2017, May ). The Burtch Works Study, Salaries of Data Scientists . https://www.burtchworks.com/files/2017/05/DS-2017-Salary-Growth.pdf. Cao, L. (2017). Data Science: Comprehensive Overview. ACM Computing Surveys 50, no.3, article 43, 1-35. Career. (2017). Data Science Careers Outlook, University of Wisconsin. https://datasciencedegree.wisconsin.edu/data-science/data-science-careers/. Forbes. (2016). Want To Become A Data Scientist? Where The Jobs Are And What Employers Are Looking For. Forbes, https://www.forbes.com/sites/emsi/2016/11/16/want-to- become-a-data-scientist-where-the-jobs-are-and-what-employers-are-looking- for/#753963535760. Forbes. (2017). IBM Predicts Demand For Data Scientists Will Soar 28% By 2020. Forbes, https://www.forbes.com/sites/louiscolumbus/2017/05/13/ibm-predicts-demand-for-data- scientists-will-soar-28-by-2020/#40d109f7e3bd. Kobielus, J. (2016). Big data analytics trends in 2017: James Kobielus’s predictions. IBM Big Data & Analytics Hub December 5, 2016, http://www.ibmbigdatahub.com/blog/big-data- and-analytics-trends-2017-james-kobielus-s-predictions. Lo, F. (2017). Big Data Salaries: An Inside Look, Data Scientist Salaries, Data Analyst Salaries, DBA Salaries, etc. https://datajobs.com/big-data-salary. O'Reilly. (2016). 2016 Data Science Salary Survey, Tools, Trends, What Pays (and What Doesn’t) for Data Professionals. http://www.oreilly.com/data/free/files/2016-data- science-salary-survey.pdf. Sentz, R. (2016). Want to Become a Data Scientist? Where the Jobs are and What Employers are Looking for. Forbes Magazine November 16, 2016, https://www.forbes.com/sites/emsi/2016/11/16/want-to-become-a-data-scientist-where- the-jobs-are-and-what-employers-are.

Appendix A: Course Descriptions For Existing Required Courses

ENG 1101 – English Composition (3 lecture/week, 3 credits) This is a course in effective essay writing and basic research techniques, including use of the library. Demanding readings are assigned for classroom discussion and as a basis for essay writing.

ENG 1121 – English Composition II (3 lecture/week, 3 credits) This is an advanced course in communication skills, including the expository essay and the research essay. This course further develops students’ reading and writing skills through literary and expository readings. Pre-requisites: ENG 1101

CST 1100 – Foundations of Computing (2h lecture/week + 2h laboratory, 3 credits) This introductory course for noncomputer systems majors highlights the use of computers in problem solving and enhances critical thinking skills. Students learn fundamental computer principles and study how technology influences our learning, communication, and social interactions. Prerequisite: CUNY proficiency in mathematics, reading and writing

CST 1101 – Problem Solving with Computer Programming (2h lecture/week + 2h laboratory, 3 credits) Introduces concepts of problem solving using constructs of logic inherent in computer programming languages. Augmented by high-level computer tools, enabling solutions to common algorithmic problems. Use of flowcharts to diagram problem solutions. Object oriented packages, flowcharting tools and viewing generated software code. Prerequisite: CUNY proficiency in mathematics, reading and writing

CST 1201 – Programming Fundamentals (2h lecture/week + 2h laboratory, 3 credits) Introduction to computer programming using the Java language. Fundamentals of Java programming language including control structures and user-defined methods. Concepts of object- oriented programming. Create simple Graphic User Interfaces and web applications. Some Java libraries will be introduced in developing application projects. Prerequisites: CST 1100 or CST 1101

CST 1204 – Database Systems Fundamentals (2h lecture/week + 2h laboratory, 3 credits) This course will introduce the student to ANSI standard Structured Query Language (SQL). The course will cover the various syntax that governs this language. In-depth discussions and practice will be given so that the student will be able to manipulate (insert, update, delete and retrieve) data in a relational database. Prerequisites: CST 1100 or CST 1101

CST 2309 – Web Programming I (2h lecture/week + 2h laboratory, 3 credits) This course focuses on how to design and maintain interactive and dynamic websites using HTML, Cascading Style Sheets (CSS) and client-side scripting with JavaScript. The students will also learn basic Web Page design principles. The goal is to develop effective, pleasing and useful websites. In the JavaScript part of the course, students will develop real-world projects to learn JavaScript programming, the JavaScript Object Model, JavaScript event handlers and how to integrate JavaScript programs in an HTML document. Students will apply this knowledge to create popup windows and scrolling messages as well as to validate forms and enhance the use of images and form objects. Client-side scripting technology will also be used to create cookies and shopping cart applications. Prerequisites: CST 1201

CST 2410 – Introduction to Computer Security (2h lecture/week + 2h laboratory, 3 credits) This course is a practical guide to security issues facing computer professionals today. Students will acquire the knowledge and skills to maintain the integrity, authenticity, availability and privacy of data. It covers computer viruses, authentication models, certificates, group policy, cryptography and access control. It also introduces the fundamental security issues of programming, database and web server. Other topics include how to monitor the system for suspicious activity and fend off attacks, keep spies and Spam out of email, take ultimate control of security by encrypting data, design Active directory, blocking ports and locking down the registry. Prerequisites: CST 2307 (Equivalent to old course CST 3510)

CST 3513 – Object Oriented Programming: Java (2h lecture/week + 2h laboratory, 3 credits) This course introduces the fundamentals of object-oriented programming. Through intensive project assignments, students will master the concept and implementation of object-oriented programming which include programmer-defined data types, class inheritances and polymorphisms, abstract classes and interfaces. Building upon the knowledge learned in the CST1201 Programming Fundamentals, students will implement object-oriented programming in the Java programming language. Exception handling and Binary IO also will be introduced. Prerequisite: CST 1201 (Acceptable substitute for CST 3506 or CST 3508) CST 3650 – Data Structures (2h lecture/week + 2h laboratory, 3 credits) Introduces structure of data in computer memory, including arrays, linked lists, stacks, trees, hash tables, graphs. Discuss algorithms to manipulate data in these structures in various ways, such as searching for a data item and sorting a set of data elements. Covers algorithms such as how to organize data elements, how to delete, insert, edit and search for a data element in a specific data structure, how to sort a set of data elements and the differences between different data structures and when to use the right ones to solve problems. Prerequisites: CST 3503 or CST 3513

CST 4724 – Database Administration (2h lecture/week + 2h laboratory, 3 credits) Students in this course will develop a fundamental understanding of the tasks and issues associated with database administration including: planning, building, tuning, troubleshooting, securing and

monitoring databases. Students will learn how to manage users, privileges, and resources, implement basic backup and recovery procedures and identify tuning opportunities. Prerequisite: CST 2405 or CST 2415 or CST 3604

MAT 1475 – Calculus I (4h lecture/week, 4 credits) Topics include functions, limits, differentiation, tangent lines, L’Hôpital’s Rule, Fundamental Theorem of Calculus and Applications. Pre-requisites: MAT 1375

MAT 1575 – Calculus II (4h lecture/week, 4 credits) A continuation of MAT 1475. Topics include Taylor polynomials, Mean Value Theorem, Taylor and Maclaurin series, tests of convergence, techniques of integration, improper integrals, areas, volumes and arc lengths. Pre-requisites: MAT 1475

MAT 2572 – Probability and Mathematical Statistics I (4h lecture/week, 4 credits) The study of discrete and continuous probability distributions including the Binomial, Poisson, Hypergeometric, Exponential, Chi-Squared and Normal Distribution. Conditional distributions, 26 covariance and correlation, confidence intervals, least square estimation, chi-square goodness of fit distribution and test for independence and randomness. Ends with an application to queuing. Prerequisite: MAT 1575

MAT 2580 – Introduction to Linear Algebra (3h lecture/week, 3 credits) An introductory course in Linear Algebra. Topics include vectors, vector spaces, systems of linear equations, linear transformations, properties of matrices, determinants, eigenvalues and eigenvectors. Pre- or corequisite: MAT 1575

Appendix B: New Courses B.1 Descriptions of New Courses

Course Code: CST 2312 Course Title: Information and Data Management I Number of hours, credits: 2 lecture hours and 2 lab hours; 3 credits Pre-requisites: CST 1101 Description: This course provides students with the introduction to the necessary informatics and intellectual tools to become efficient and effective information users. The course covers topics related to the digital infrastructure, acquisition, organization, management and curation of data. The course is structured around the Python tools for regular expression analysis, accessing data sources (crawling, Web APIs), analysis of structured data. At the end of the class the students complete a project to demonstrate the mastery of the technical topics discussed in class with an application to their domain of interest.

Course Code: CST 2402 Course Title: Introduction to Data Science Number of hours, credits: 2 lecture hours and 2 lab hours; 3 credits Pre-requisites: CST 1204, CST 1201, Pre- or Co-req. MAT 1475 Description: This is a fundamental course in the basic concepts, principles and ethical issues of the Data Science domain including: definition, framework, techniques, issues, business uses and ethics. Topics include data collection, processing and management, exploratory data analysis, data visualization and presentation and ethical issues. Projects involve the use of data science techniques and tools to solve business problems and improve business decision making.

Course Code: CST 3502 Course Title: Data Mining Number of hours, credits: 2 lecture hours and 2 lab hours; 3 credits Pre-requisites: CST 2312, CST 2402 Description: Theory and practice of data mining methods for extracting knowledge from data using state-of-the-art software (WEKA) for solving scientific and business problems will be covered. Topics discussed in the class include: data preprocessing, classification and prediction, association mining, and cluster analysis. Projects either involve research in data mining or address a practical problem using the methods and tools introduced in the class.

Course Code: CST 3512 Course Title: Information and Data Management II Number of hours, credits: 2 lecture hours and 2 lab hours; 3 credits Pre-requisites: CST 2312 Description: This course continues to familiarize students with the informatics and intellectual tools necessary for students to become efficient and effective information users. The course covers topics related to the digital infrastructure, management and curation of data both structured (record-based) and unstructured (such as text). For structured data (data series, data frames), the course introduces Time Series data analysis and the basics of data visualization; for unstructured data (text), the course introduces text mining techniques. The course is project-based and case studies of using Information and Data Management are discussed. Based on these case studies the students develop their own projects where they choose a data set, use the appropriate data processing techniques for data analysis, and present their finding and conclusions reached using the information and data management and analysis techniques discussed in class.

Course Code: CST 3602 Course Title: Data Visualization Number of hours, credits: 2 lecture hours and 2 lab hours; 3 credits Pre-requisites: CST 3512, MAT 2572 Description: This course introduces information visualization basics and provides knowledge about design and information literacy perspective, including what makes a good and bad visualization. The course covers basic visualization design techniques and evaluation principles, techniques for visualizing multivariate, temporal, text-based, geospatial, hierarchical, and network/graph-based data. The final project for the course is to acquire, parse, analyze a large dataset, and create meaningful, good visualization for this dataset.

Course Code: CST 4702 Course Title: Machine Learning Fundamentals Number of hours, credits: 2 lecture hours and 2 lab hours; 3 credits Pre-requisites: CST 3650, CST 3502, MAT 2572 Description: This course introduces fundamental machine learning algorithms and techniques, and their implementations applied to solving real world problems. Topics covered include Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks), unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning) and best practices in machine learning (bias/variance theory; innovation process in machine learning). The theory of machine learning and the practical know-how lead to numerous case studies with applications in text understanding (web search, anti-spam), medical informatics, audio, database mining, and other areas.

Course Code: CST 4802 Course Title: Information Retrieval Number of hours, credits: 2 lecture hours and 2 lab hours; 3 credits Pre-requisites: CST 4702 Description: The course details basic and advanced concepts and principles of information retrieval including: definition, framework, uses and ethical issues. Issues related to interface, relevance, language processing, and indexing are discussed. Information retrieval techniques covering both effectiveness and run-time performance are covered as well as heuristics and algorithms used in information retrieval systems. Information retrieval mechanism used in both Websites and Web search engines are also discussed. Ethical issues including personal privacy, bias, informed consent, monitoring and surveillance are also examined and discussed.

Course Code: CST 4812 Course Title: Natural Language Processing Number of hours, credits: 2 lecture hours and 2 lab hours; 3 credits Pre-requisites: CST 3512, CST 2572 Description: This course introduces a broad range of techniques that aim to read, understand, and extract information from natural language text resources. The course covers both knowledge- based and statistical approaches for syntax, semantics, and discourse. In addition to introducing theoretical aspects and methods of natural language processing (NLP), the course covers multiple applications such as Machine Translation, Summarization, Interactive Dialog Systems, etc.

B.2 Syllabi of New Courses

B.2.1: CST2312 Information and Data Management I

3 credits ( 2 lecture hours and 2 lab hours)

Course Description: This course provides students with the introduction to the necessary informatics and intellectual tools to become efficient and effective information users. The course covers topics related to the digital infrastructure, acquisition, organization, management and curation of data. The course is structured around the Python tools for regular expression analysis, accessing data sources (crawling, Web APIs), analysis of structured data. At the end of the class the students complete a project to demonstrate the mastery of the technical topics discussed in class with an application to their domain of interest.

Pre-requisites: CST 1101 Problem Solving with Computer Programming

Course Objectives: Upon successful completion of this course, students should be able to: ● Demonstrate knowledge of regular expressions ● Demonstrate knowledge of web APIs ● Demonstrate the skills of processing information downloaded from Internet ● Demonstrate knowledge of web crawling

Software: Python and Python Libraries

General Education Outcomes: ● SKILLS/Inquiry/Analysis: Students will employ scientific reasoning and logical thinking.

● SKILLS/Communication: Students will communicate in diverse settings and groups, using written (both reading and writing), oral (both speaking and listening), and visual means

● VALUES, ETHICS, RELATIONSHIPS / Professional/Personal Development: Students will work with teams, including those of diverse composition. Build consensus. Respect and use creativity.

Required Text: ● Python for Everybody: Exploring Information by Charles R. Severance Free on-line version: http://www.pythonlearn.com/book.php#python-for-informatics ● Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by W McKinney

Additional Reading Materials: The instructor will identify several additional information resources during the semester including Internet resources, print material (handouts) and reference books.

Tools and On-Line Documentation: ● Python: https://www.python.org/ ● Python Data Analysis Library: http://pandas.pydata.org/ ● Python package for scientific computing: http://www.numpy.org/ ● IPython command shell for interactive computing: https://ipython.org/ ● Anaconda Data Science ecosystem: https://www.continuum.io/

Grading Procedure: Midterm Exam 20% Final Exam 30% Lab Final Project 25% Homework assignments 20% In-class labs/quizzes 5% ===== TOTAL 100%

Letter A A- B+ B B- C+ C D F Grade Numerical 93- 90- 87- 83- 80- 77- 70- 60- <=59.9 Grade 100 92.9 89.9 86.9 82.9 79.9 76.9 69.9

The grade distribution follows the information in the NYCCT Student Handbook (p.43).

Academic Integrity: During the course of the class you are required to follow the NYCCT Academic Integrity Standards described in the Student Handbook (pp.95 – 99)

NYCCT Student Handbook can be downloaded here: http://www.citytech.cuny.edu/current- student/docs/StudentHandbook.pdf.

Course Outline: Wee Topics Assignments and Readings k 1 Review of Python Basics: http://www.pythonlearn.com/html- variables, basic data types (String, int, float, 270/book003.html Boolean, None), lists, selection, loops, functions 2 Introduction to IPython and IPython Lecture notes NoteBooks Assignment 1 3 Introduction to Python tuples, and http://www.pythonlearn.com/html- dictionaries 270/book011.html Lab exercises: tuples, dictionaries 4,5 Introduction to regular expressions http://www.pythonlearn.com/html- Lab exercises: regular expressions 270/book012.html Assignment 2 6 Working with files; Using Libraries http://www.pythonlearn.com/html- Lab exercises: importing libraries, using 270/book008.html functions from imported libraries Assignment 3 7 Midterm Exam: http://www.pythonlearn.com/html- Python tuples, dictionaries, regular 270/book010.html expressions http://www.pythonlearn.com/html- 270/book012.html

8 Introduction to Web APIs Assignment 4 Web APIs using Python Lab exercises: using web APIs 9 Regular Expressions and Crawling 10 Reading CSV files, Selecting rows/columns Assignment 5 11, Pandas and NumPy: 12 Introduction to data series, data frames Lab exercises: numpy arrays, pandas dataframes 13, Lab: Working on a project 14 15 Review, project presentation, final exam

ASSESSMENT CRITERIA: For successful completion of this course the student should be able to:

For the successful completion of this Evaluation methods and criteria course a student should be able to: Demonstrate knowledge of regular Students will create Python scripts (and run expressions Python commands in the Shell mode) that use regular expressions to extracts and/or modify

information in the source file / string. Demonstrate knowledge of web APIs Students will create Python scripts (and run Python commands in the Shell mode) that use web APIs to upload locally wen pages. Demonstrate the skills of processing Students will create Python scripts (and run information downloaded from Internet Python commands in the Shell mode) that read the source text of a downloaded web page and extract the desired information. Demonstrate knowledge of Students will create Python scripts that can access the information form the web pages from web crawling the sites that do not have a custom web API. Work effectively in a team The final project is a team project. The students will group into teams and create a project that demonstrates their knowledge of the programming tools and techniques learned in class. Will use the Internet and other resources to complete the project. Also, there will be an oral presentation made to the class. It will include their learning experience in working in a group.

GENERAL EDUCATION LEARNING OUTCOMES/ASSESSMENT METHODS

LEARNING OUTCOMES ASSESSMENT METHODS

1. Demonstrate the ability to work 1. Classroom discussions, group collaboratively and independently on assignments and individual oral assignments in and outside a classroom presentations. setting. 2. Understand and employ both 2. Classroom discussion, group activities, quantitative and qualitative analysis group presentations, quizzes, tests, final exam. to solve problems.

3. Develop reading, writing 3. Biweekly reading and writing assignments, competencies, and listening skills. individual and group presentation, classroom discussion. Each homework assignment requires writing.

4. Work with teams. Build 4. Group projects and presentations. consensus. Use creativity.

Bibliography: • Charles R. Severance (2013). Python for Informatics: Exploring Information. Publisher: CreateSpace Independent Publishing Platform; 1 edition, ISBN-10: 1492339245 Free on-line version: http://www.pythonlearn.com/book.php#python-for-informatics

• Wes McKinney (2017) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. Publisher: O'Reilly Media; 2 edition (October 20, 2017) ISBN-10: 1491957662

• Armando Fandango (2017). Python Data Analysis Publisher: Packt Publishing - ebooks Account; 2nd Revised edition edition (March 27, 2017) ISBN-10: 1787127486

• Jake VaderPlas (2016). Python Data Science Handbook: Essential Tools for Working with Data. Publisher: O'Reilly Media; 1 edition (December 10, 2016) ISBN-10: 1491912057

B.2.2: CST2402 Introduction to Data Science

3 credits (2 lecture hours and 2 lab hours)

Course description: This is a fundamental course in the basic concepts, principles and ethical issues of the Data Science domain including: definition, framework, techniques, issues, business uses and ethics. Topics include data collection, processing and management, exploratory data analysis, data visualization and presentation and ethical issues. Projects involve the use of data science techniques and tools to solve business problems and improve business decision making.

Prerequisites: The successful completion of the following courses: CST1201, CST1204

Pre or Co requisites: MAT1475

Learning Outcomes: Upon successful completion of this course, the students acquire the following knowledge and skills: 1. Define and analyze the issues and challenges related to the data science domain including issues related to machine learning, data mining and data visualization. 2. Identify, analyze and use data science strategies and tools to solve business problems and improve business decision making. 3. Identify and examine ethical issues related to the data science domain, particularly issues related to privacy, data sharing and algorithmic decision-making.

General education outcomes: ● SKILLS/Inquiry/Analysis: Students use scientific reasoning and logical thinking. ● SKILLS/Communication: Students use written (both reading and writing), oral (both speaking and listening), and visual means to communicate. ● VALUES, ETHICS, RELATIONSHIPS / Professional/Personal Development: Students work in diverse teams utilizing key traits including respect, cooperation and creativity.

Required textbook Cielen, Davy, Arno D.B. Meysman, and Mohamed Ali. Introducing Data Science, Big Data, Machine Learning and More, Using Python Tools. Shelter Island: Manning, 2016. ISBN-13: 9781633430037

Additional materials The instructor will identify several additional information resources during the semester including Internet resources, print material (handouts), journal articles and reference books.

appropriately to infractions of academic integrity. Accordingly, academic dishonesty is prohibited in The City University of New York and at New York City College of Technology and is punishable by penalties, including failing grades, suspension, and expulsion. The complete text of the College policy on Academic Integrity may be found in the catalog.

Grading procedure: Tests (3) 30% Final Exam 15% Labs 15% Assignments 35% Participation 5% ===== TOTAL 100%

Letter A A- B+ B B- C+ C D F Grade Numerical 93- 90- 87- 83- 80- 77- 70- 60- <=59.9 Grade 100 92.9 89.9 86.9 82.9 79.9 76.9 69.9

COURSE OUTLINE: Week Topics Reading Labs/ Tests Assignments 1 Introduction to Data Science Ch. 1 Lab 1: Data ● Benefits and uses of data science Science in and Big Data business ● Types of Data ● Data gathering, exploration, modeling and presentation ● Ethical issues 2 Data Science Process Ch. 2 Lab 2: The DS ● Define goals and create plan Process ● Retrieve data; clean, integrate and transform data ● Exploratory data analysis ● Build models and present findings 3 Big Data Ch. 4 Lab 3: Big Data ● Challenges of Big Data ● Algorithms and data structure ● Selecting tools ● Case Study 4 Big Data Techniques and Tools Ch. 5 Lab 4: Tools Test 1 ● Distributing data storage and and Techniques processing with frameworks

(Hadoop, HDFS, and Map- Assignment 1 Reduce) ● Case Study 5 NoSQL Ch. 6 Lab 5: NoSQL ● Introduction to NoSQL categories ● Case Study 6 Data on the Web and Linked Data Ch. 7 Lab 6: Linked ● Introduction to connected data and Data graph database ● Introduction to RDF/RDFS and Assignment 2 OWL ● Case Study 7 Data Mining Ch. 8 Lab 7: Practical ● Introduction to Data Mining uses of data ● Case study mining 8 Data Mining Techniques and Tools Ch. 8 Lab 8: Data Test 2 ● Bag of words Mining ● Stemming and lemmatization ● Decision tree classifier Assignment 3 9 Machine Learning Ch. 3 Lab 9: Alexa ● Introduction to machine learning and machine ● The modeling process learning 10 Machine Learning (continued) Ch. 3 Lab 10: ● Supervised and unsupervised Machine learning Learning ● Semi-supervised learning Assignment 4 11 Data Visualization, Techniques and Ch. 9 Lab 11&12: Test 3 & Tools Decision 12 ● Introduction to Data Visualization making with ● Introduction to Matplotlib visualization ● Plots, histograms, bar charts, scatterplots using Matplotlib 13 Ethics of Data Science Handouts/ Lab 13: The ● Data and discrimination Internet resources/ Good, the bad ● Algorithms and discrimination journal articles and the big data ● Data collection, storage, analysis and application ethics ● Preserving Privacy 14 Advanced Topics in Data Science Handouts/ Lab 14: Social ● Social Networks Internet resources/ Networks and ● Data Journalism journal articles Data Journalism 15 Review Final Exam

COURSE INTENDED LEARNING OUTCOMES/ASSESSMENT METHOD

LEARNING OUTCOMES ASSESSMENT METHOD 1. Demonstrate an understanding of the 1. Group activities, written technical issues and challenges related assignment, in class and to the Data Science domain. interactive online discussions 2. Identify, examine and use the Data 2. Classroom exercises and Science strategies and tools (such as programming assignments Hadoop, MapReduce, NoSQL, RDF, where students use non- Matplotlib) to retrieve, manage and relational databases such as explore data. MongoDB and JSON as well as code RDF files and SPARQL 3. Identify the ethical concerns and 3. Group activities, Written examine the challenges and issues assignment, in class and related to privacy, data sharing and interactive online discussion algorithmic decision-making

GENERAL EDUCATION LEARNING OUTCOMES/ASSESSMENT METHODS

LEARNING OUTCOMES ASSESSMENT METHODS 1. Demonstrate the ability to work collaboratively and 1. Classroom discussions, group independently on assignments in and outside a assignments and individual oral classroom setting. presentations.

2. Understand and employ both quantitative and 2. Classroom discussion, group activities, qualitative analysis to solve problems. group presentations, quizzes, tests, final exam.

4. Work with teams. Build consensus. Use 4. Group projects and presentations. creativity.

Bibliography

Cady, Field. The Data Science Handbook. Hoboken: John Wiley & Sons Inc., 2017. ISBN-13: 978-1119092940

Grus, Joel. Data Science from Scratch, First Principles with Python. Sebastopol: O’Reilly Media, Inc., 2015. ISBN-13:978-1491901427

O’Neil, Cathy and Rachel Schutt. Doing Data Science, Straight Talk from the Frontline. Sebastopol: O’Reilly Media, Inc., 2014. ISBN-13: 978-1449358655

Provost, Foster and Tom Fawcett. Data Science for Business. Sebastopol: O’Reilly Media, Inc., 2013. ISBN-13: 978-1449361327

Stanton, Jeffrey M. and Jeffrey M. Saltz. An Introduction to Data Science. New York: Sage Publishing Inc., 2017. ISBN-13: 978-1506377537

B.2.3: CST3502 Data Mining

3 credits ( 2 lecture hours and 2 lab hours)

Course Description: Theory and practice of data mining methods for extracting knowledge from data using state-of- the-art software (WEKA) for solving scientific and business problems will be covered. Topics discussed in the class include: data preprocessing, classification and prediction, association mining, and cluster analysis. Projects either involve research in data mining or address a practical problem using the methods and tools introduced in the class

Pre-requisites: CST 2402 – Introduction to Data Science and CST 2312 – Information and Data Management I

Learning Outcomes: Upon successful completion of this course, students should be able to: ● Develop familiarity with data mining techniques, and demonstrate the ability to apply these techniques to real world problems ● Demonstrate an understanding of the role and impact of data mining in our society. ● Demonstrate an understanding of the ethical and privacy issues that arise from the process of mining user data.

Software: WEKA (http://www.cs.waikato.ac.nz/ml/weka/)

General Education Outcomes: ● SKILLS/Inquiry/Analysis: Students will employ scientific reasoning and logical thinking.

● SKILLS/Communication: Students will communicate in diverse settings and groups, using written (both reading and writing), oral (both speaking and listening), and visual means

● VALUES, ETHICS, RELATIONSHIPS / Professional/Personal Development: Students will work with teams, including those of diverse composition. Build consensus. Respect and use creativity.

Required Text: "Introduction to Data Mining", P. Tan, M. Steinbach & V. Kumar. Addison Wesley. 2005.

Additional Text: “Data Science for Business”, F. Provost, T. Fawcett. 2013 Academic Integrity – Students and all others who work with information, ideas, texts, images, music, inventions, and other intellectual property owe their audience and sources accuracy and honesty in using, crediting, and citing sources. As a community of intellectual and professional workers, the College recognizes its responsibility for providing instruction in information literacy and academic integrity, offering models of good practice, and responding vigilantly and

Grading Procedure: Test1 15% Test2 15% Final Exam 20% Midterm Exam 20% Labs (in class) 10% Lab Project 10% Homework Assignments 10% ===== TOTAL 100%

Letter A A- B+ B B- C+ C D F Grade Numerical 93- 90- 87- 83- 80- 77- 70- 60- <=59.9 Grade 100 92.9 89.9 86.9 82.9 79.9 76.9 69.9

Course Outline: Week Topic Covered Lab Assignments and Readings Week 1 Introduction: what is data mining, supervised Ch1, pp. 1 – 11 vs unsupervised data mining

Data: types, concepts, instances/examples, (Ch2, stop features/attributes, target/class, outliers, etc. middle 76) Other issues related to data for data mining tasks will be discussed during the course of the class. Week 2 Data (contd.) Ch4, pp. 145 – Introduction to classification supervised 149 (4.1, 4.2), learning, features, target: linear classifier lecture slides Assignment 1: Classification. Lab 1: Linear Classifier

Week 3 HW1 discussion Ch4, pp. 150 – Inductive learning: Decision trees, Shannon’s 172 (4.3), information gain, brief math tutorial on lecture notes probabilities and entropy Due: Assignment 1

Week 4 Overfitting, evaluation (accuracy, precision, Ch4, pp. 4.4, recall, cross-validation, leave-one-out cross- lecture notes validation, brief introduction to ROC curves) Brief intro to WEKA Lab 2: WEKA Basics Week 5 Evaluation (contd.) HW2 discussion Ch.5 pp. 227 – Math tutorial: Bayesian theorem 229 Assignment 2: Decision Trees and Shannon’s Lab 3: Decision information. Trees Week 6 HW2 discussion Ch.5 pp. 227 – Math tutorial: Bayesian theorem 229 Lecture notes Midterm review Ch5, 5.1 Rule-based classifier Due: Assignment 2

Week 7 Midterm Ch.5 pp. 229 – Midterm Revision 238 Week 8 Naïve Bayesian classifier Ch5, 5.2 Nearest neighbor Lab 4: Naïve Bayes Week 9 HW3 discussion Ch8 8.1-8.4 Nearest neighbor (contd.) (skip 8.2.6, Unsupervised learning, similarity, example- 8.3.3, 8.3.4) based learning, clustering Assignment 3: Clustering (using WEKA). Week 10 Clustering (contd.) Browse pp. Association rule mining 325 – 341 Week 11 Brief introduction to Deep Learning Lecture notes HW4 discussion Due: Assignment 3 Ethical Data Mining: Privacy sensitivity mining Week 12 Natural Language learning (contd.) Lecture notes

Project presentations Week 13 Logistic Regression Lab 5: Logistic Regression Week 14 Term Project Presentations Week 15 Finals

COURSE INTENDED LEARNING OUTCOMES/ASSESSMENT METHODS

LEARNING OUTCOMES ASSESSMENT METHODS 1. Understand of data mining concepts, 1. Group activities, written report. supervised vs unsupervised data mining 2. Implement a classification technique such as 2. Individual oral presentations, in‐class Decision trees, SVMs. group activities.

3. Understand Overfitting, Evaluation 3. Classroom discussion, in‐ (accuracy, precision, recall, cross-validation, class estimation group leave-one-out cross- validation, brief assignments. introduction to ROC curves) 4. Demonstrate an understanding on how data 4. Classroom discussion, in‐class group mining is applied on Clustering and assignments learning logs. association mining applications 5. Demonstrate an understanding of the role 5. Individual short essay related to and impact of data mining in our society. functions (e.g., population growth, economics, climate change). 6. Demonstrate an understanding of the ethical 6. Classroom discussion on the ethical and privacy issues that arise from the issues of compromising the privacy of process of mining user data. user data based on real world case st dies 7. Demonstrate an understanding of basic 7. Written report and group presentation introductory concepts on deep learning and (e.g., an analysis of the garbage patch in natural language processing. the Pacific Ocean), learning logs.

GENERAL EDUCATION LEARNING OUTCOMES/ASSESSMENT METHODS

LEARNING OUTCOMES ASSESSMENT METHODS 1. Demonstrate the ability to work 1. Classroom discussions, group assignments collaboratively and independently on and individual oral presentations. assignments in and outside a classroom setting. 2. Understand and employ both quantitative 2. Classroom discussion, group activities, and qualitative analysis to solve problems. group presentations, quizzes, tests, final exam.

3. Develop reading, writing competencies, 3. Biweekly reading and writing and listening skills. assignments, individual and group presentation, classroom discussion. Each homework assignment requires writing.

4. Work with teams. Build consensus. Use 4. Group projects and presentations. creativity.

Bibliography:

Bezdek, J. C., & Pal, S. K. (1992). Fuzzy models for pattern recognition: Methods that search for structures in data. New York: IEEE Press

Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (Eds.). (1996). Advances in knowledge discovery and data mining. AAAI/MIT Press.

Han, J., & Kamber, M. (2000). Data mining: Concepts and techniques: Morgan Kaufmann.

Hastie, T., Tibshirani, R., & Friedman, J. H. (2001). The elements of statistical learning: Data mining, inference, and prediction: New York: Springer.

Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. New Jersey: Prentice Hall.

Jensen, F. V. (1996). An introduction to bayesian networks. London: University College London Press.

Kaufman, L., & Rousseeuw, P. J. (1990). Finding groups in data: An introduction to cluster analysis. New York: John Wiley.

Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine learning, neural and statistical classification: Ellis Horwood.

B.2.4: CST3512 Information and Data Management II

3 credits ( 2 lecture hours and 2 lab hours)

Course Description:

This course continues to familiarize students with the informatics and intellectual tools necessary for students to become efficient and effective information users. The course covers topics related to the digital infrastructure, management and curation of data both structured (record-based) and unstructured (such as text). For structured data (data series, data frames), the course introduces Time Series data analysis and the basics of data visualization; for unstructured data (text), the course introduces text mining techniques. The course is project-based. During the course several case studies of using Information and Data Management are discussed. Based on these case studies the students develop their own projects where they choose a data set, use the appropriate data processing techniques for data analysis, and present their finding and conclusions reached using the information and data management and analysis techniques discussed in class.

Pre-requisites: CST 2312 Information and Data Management I

Final Project: The final project is a team project with an oral presentation. The teams choose an on-line data collection, obtain this data collection, analyze it, and create a visualization supporting interesting facts found in this data collection.

Course Objectives: Upon successful completion of this course, students should be able to: ● Demonstrate knowledge of web APIs ● Demonstrate the skills of processing information downloaded from Internet ● Demonstrate knowledge of web crawling ● Demonstrate knowledge of basic text analysis techniques ● Demonstrate knowledge of basic visualization techniques

Software: Python and Python Libraries

General Education Outcomes: ● SKILLS/Inquiry/Analysis: Students will employ scientific reasoning and logical thinking.

● SKILLS/Communication: Students will communicate in diverse settings and groups, using written (both reading and writing), oral (both speaking and listening), and visual means

● VALUES, ETHICS, RELATIONSHIPS / Professional/Personal Development: Students will work with teams, including those of diverse composition. Build consensus. Respect and use creativity.

Required Text: ● Charles R. Severance. Python for Everybody: Exploring Information. ISBN-13: 978-1492339243 Free on-line version: http://www.pythonlearn.com/book.php#python-for-informatics

● Wes McKinney. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython, 2nd edition ISBN-13: 978-1491957660

Additional Reading Materials: The instructor will identify several additional information resources during the semester including Internet resources, print material (handouts) and reference books.

Grading Procedure: Midterm Exam 20% Final Exam 25% Lab Project 20% Homework assignments 25% In-class labs/quizzes 10% ===== TOTAL 100%

Letter A A- B+ B B- C+ C D F Grade Numerical 93- 90- 87- 83- 80- 77- 70- 60- <=59.9 Grade 100 92.9 89.9 86.9 82.9 79.9 76.9 69.9

The grade distribution follows the information in the NYCCT Student Handbook (p.43).

Academic Integrity: During the course of the class you are required to follow the NYCCT Academic Integrity Standards described in the Student Handbook (pp.95 – 99)

NYCCT Student Handbook can be downloaded here: http://www.citytech.cuny.edu/current- student/docs/StudentHandbook.pdf.

Course Outline:

Week Topics Assignments and Readings 1, 2 Review of Python Pandas basics: Lab 1: IPython – Data Series and Data data series and data frames (CST Frames 2302) using IPython Notes 3, 4 More on data series and data frames Assignment 1 Lab: data series, pandas dat frames 5 Basics of visualization using Severance: Ch. 15 MatPlotLib Lab 2: MatPlotLib for Visualization Lab: exercises using matplotlib library 6,7 Introduction to text analysis McKinney: Ch. 6, Ch. 7.3 Lab 3: Text analysis using search engine data Severance: Ch. 11 8 Midterm Exam; Assignment 2 Introduction to Time Series data Lab 4: Time series data analysis analysis, storing data over time 9 Introduction to Time Series data McKinney: Ch. 11 analysis, storing data over time Lab: time series analysis 10 Lab: Practice with a sample API (e.g., https://www.citibikenyc.com/system-data Citibike API), part I; Team project: proposals 11 Practice with a sample API (e.g., https://www.citibikenyc.com/system-data Citibike API), part II; Assignment 3 Lab team project: data collection from Lab 5: Citibike API practice on-line resources 12 Practice with a sample API (suggested Lab 6: Data Collection from online in class); resources Lab team project: data collection from on-line resources 13 Team project: data analysis and Lab 6: Data Analysis visualization 14 Final exam review; Final exam (the final exam is cumulative) 15 Final Project Presentations / Discussion

ASSESSMENT CRITERIA: For successful completion of this course the student should be able to: For the successful completion of this Evaluation methods and criteria course a student should be able to: Demonstrate knowledge of web APIs Students will create Python scripts (and run Python commands in the Shell mode) that use web APIs to upload locally wen pages. Demonstrate the skills of processing Students will create Python scripts (and run information downloaded from Internet Python commands in the Shell mode) that read the source text of a downloaded web page and extract the desired information. Demonstrate knowledge of Students will create Python scripts that can access the information form the web pages from web crawling the sites that do not have a custom web API. Demonstrate knowledge of basic text Students will create Python scripts that analyze analysis techniques text information in the source files (e.g., find most frequent words, find most important entities mentioned in text, etc.) Demonstrate knowledge of basic Students will create Python scripts that visualize visualization techniques the input data. Work effectively in a team The final project is a team project. The students will group into teams and create a project that demonstrates their knowledge of the programming tools and techniques learned in class. Will use the Internet and other resources to complete the project. Also, there will be an oral presentation made to the class. It will include their learning experience in working in a group.

GENERAL EDUCATION LEARNING OUTCOMES/ASSESSMENT METHODS

LEARNING OUTCOMES ASSESSMENT METHODS 1. Demonstrate the ability to work collaboratively 1. Classroom discussions, group and independently on assignments in and outside assignments and individual oral a classroom setting. presentations.

2. Understand and employ both quantitative and 2. Classroom discussion, group qualitative analysis to solve problems. activities, group presentations, quizzes, tests, final exam.

3. Develop reading, writing competencies, and 3. Biweekly reading and writing listening skills. assignments, individual and group presentation, classroom discussion. Each homework assignment requires writing. 4. Work with teams. Build consensus. Use 4. Group projects and presentations. creativity.

• Wes McKinney (2017) Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython. Publisher: O'Reilly Media; 2 edition (October 20, 2017) ISBN-10: 1491957662

• Armando Fandango (2017). Python Data Analysis Publisher: Packt Publishing - ebooks Account; 2nd Revised edition edition (March 27, 2017) ISBN-10: 1787127486

• Jake VaderPlas (2016). Python Data Science Handbook: Essential Tools for Working with Data. Publisher: O'Reilly Media; 1 edition (December 10, 2016) ISBN-10: 1491912057

B.2.5: CST3602 Data Visualization 3 credits (2 lecture hours and 2 lab hours)

Course Description: This course introduces information visualization basics and provides knowledge about design and information literacy perspective, including what makes good and bad visualization. The course covers basic visualization design techniques and evaluation principles, techniques for visualizing multivariate, temporal, text-based, geospatial, hierarchical, and network/graph-based data. The final project for the course is to acquire, parse, analyze a large dataset, and create meaningful, good visualization for this dataset.

Pre-requisites: CST 3512 (Information and Data Management II) and MAT 2572 (Probability and Mathematical Statistics I)

Final Project: The final project will be a team project with an oral presentation. The teams choose an on-line data collection, obtain this data collection, analyze it, and create a visualization supporting interesting facts found in this data collection.

Course Objectives: Upon successful completion of this course, students should be able to: ● Demonstrate knowledge of the issues and challenges (including, ethics issues) of data / information visualization. ● Demonstrate knowledge of the fundamentals of good visualization. ● Analyze data visualization examples and reason about the quality of these visualization examples. ● Create data visualizations following the principles of good visualization.

Software: Python and Python Libraries

General Education Outcomes: ● SKILLS/Inquiry/Analysis: Students will employ scientific reasoning and logical thinking.

● SKILLS/Communication: Students will communicate in diverse settings and groups, using written (both reading and writing), oral (both speaking and listening), and visual means

● VALUES, ETHICS, RELATIONSHIPS / Professional/Personal Development: Students will work with teams, including those of diverse composition. Build consensus. Respect and use creativity.

Required Text: Alberto Cairo. The Truthful Art: Data, Charts, and Maps for Communication ISBN-13: 0321934075

Additional Reading Materials: ● Edward Tufte. The Visual Display of Quantitative Information ISBN-13: 0961392142 ● Cole Nussbaumer Knaflic. Storytelling with Data: A Data Visualization Guide for Business Professionals ISBN-13: 978-1119002253 The instructor will identify several additional information resources during the semester including Internet resources, print material (handouts) and reference books.

Grading Procedure: Midterm Exam 20% Final Exam 30% Lab Final Project 25% Homework assignments 20% In-class labs/quizzes 5% ===== TOTAL 100%

Letter A A- B+ B B- C+ C D F Grade Numerical 93- 90- 87- 83- 80- 77- 70- 60- <=59.9 Grade 100 92.9 89.9 86.9 82.9 79.9 76.9 69.9

The grade distribution follows the information in the NYCCT Student Handbook (p.43). Academic Integrity: During the course of the class you are required to follow the NYCCT Academic Integrity Standards described in the Student Handbook (pp.95 – 99)

NYCCT Student Handbook can be downloaded here: http://www.citytech.cuny.edu/current- student/docs/StudentHandbook.pdf.

Course Outline: Week Topics Assignments and Readings 1 Introduction Alberto Cairo Importance of Ethics in Visualization. Implications of (Good / Bad) Data Visualization. Examples and discussion of bad data visualization. 2, 3 Review of Matplotlib basics (CST3502 http://pandas.pydata.org/pandas- Information and Data Management II) docs/stable/whatsnew.html#restore- Lab: Plotting, axes, legend placement matplotlib-datetime-converter- registration

4 Axes customization Assignment 1 Lab: axes customization for various plots and diagrams 5 Histograms (for different types of http://pandas.pydata.org/pandas- distributions) docs/stable/10min.html#histogramming Lab: creating histograms 6 Legends, annotation, and styles Assignment 2 Labs: customizing legends, annotations, style 7,8 Bar Charts: http://pandas.pydata.org/pandas- choosing the plotted data intervals, docs/stable/style.html#Bar-charts adding confidence intervals Lab: creating bar charts Midterm 9, 10 Analyzing and plotting time series data: Assignment 3 line plots, dot plots, density plots, whisker plots by interval Lab: creating charts and graphs for times series analysis 11 Heatmaps Lab: creating heatmaps 12 Time series with moving images http://pandas.pydata.org/pandas- docs/stable/timeseries.html 13 Lab: Case study 14 Lab: Working on a team project 15 Review, project presentation, final exam

ASSESSMENT CRITERIA: For successful completion of this course the student should be able to: For the successful completion of this course a Evaluation methods and criteria student should be able to: 1. Demonstrate knowledge of the issues and Exams, homework assignments, in-class challenges of data / information lab exercises and project visualization. 2. Demonstrate knowledge of the fundamentals of good visualization. 3. Analyze data visualization examples and reason about the quality of these visualization examples. 4. Create data visualizations following the principles of good visualization.

GENERAL EDUCATION LEARNING OUTCOMES/ASSESSMENT METHODS

LEARNING OUTCOMES ASSESSMENT METHODS

1. Demonstrate the ability to work 1. Classroom discussions, group assignments collaboratively and independently on and individual oral presentations. assignments in and outside a classroom setting. 2. Understand and employ both quantitative and 2. Classroom discussion, group activities, group qualitative analysis to solve problems. presentations, quizzes, tests, final exam. 3. Develop reading, writing competencies, and 3. Biweekly reading and writing assignments, listening skills. individual and group presentation, classroom discussion. Each homework assignment requires writing. 4. Work with teams. Build consensus. Use 4. Group projects and presentations. creativity.

Bibliography: • Alberto Cairo. The Truthful Art: Data, Charts, and Maps for Communication Publisher: New Riders; 1 edition (February 28, 2016) ISBN-13: 0321934075 • Edward Tufte (2001). The Visual Display of Quantitative Information Publisher: Graphics Press; 2nd edition edition (May 1, 2001) ISBN-13: 0961392142 • Cole Nussbaumer Knaflic (2015). Storytelling with Data: A Data Visualization Guide for Business Professionals Publisher: Wiley ISBN-13: 978-1119002253

B.2.6: CST4702 Machine Learning Fundamentals 3 credits (2 lecture hours and 2 lab hours)

Course Description: This course introduces fundamental machine learning algorithms and techniques, and their implementations applied to solving real world problems. Topics covered include Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks), unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning) and best practices in machine learning (bias/variance theory; innovation process in machine learning). The theory of machine learning and the practical know-how lead to numerous case studies with applications in text understanding (web search, anti-spam), medical informatics, audio, database mining, and other areas.

Pre-requisites: CST3650: Data Structures, CST3502 Data Mining and MAT2572: Statistics and Probability I

Learning Outcomes: Upon successful completion of this course, students should be able to: ● Demonstrate an understanding of univariate and multivariate linear regression and apply it to machine learning. ● Use tools which are important to implement machine learning solutions like Octave and/or Matlab ● Demonstrate an understanding of logistic regression and apply it to machine learning. ● Demonstrate an understanding of the differences between supervised and unsupervised learning. ● Demonstrate an understanding of the structure of neural networks and their use to solve problems ● Demonstrate an understanding of dimensionality reduction using principal components analysis ● Demonstrate an understanding of anomaly detection techniques. ● Demonstrate an understanding if clustering analysis techniques.

General Education Outcomes: ● SKILLS/Inquiry/Analysis: Students will employ scientific reasoning and logical thinking.

● SKILLS/Communication: Students will communicate in diverse settings and groups, using written (both reading and writing), oral (both speaking and listening), and visual means

● VALUES, ETHICS, RELATIONSHIPS / Professional/Personal Development: Students will work with teams, including those of diverse composition. Build consensus. Respect and use creativity.

Required Text: Ethem Alpaydin, Introduction to Machine Learning, Third Edition ISBN- 10: 8120350782, ISBN-13: 978-8120350786

Recommended Texts: ● Stephen Marsland, Machine Learning: An Algorithmic Perspective ● Christopher M. Bishop, Pattern Recognition and Machine Learning ● Tom Mitchell, Machine Learning

Academic Integrity – Students and all others who work with information, ideas, texts, images, music, inventions, and other intellectual property owe their audience and sources accuracy and honesty in using, crediting, and citing sources. As a community of intellectual and professional workers, the College recognizes its responsibility for providing instruction in information literacy and academic integrity, offering models of good practice, and responding vigilantly and appropriately to infractions of academic integrity. Accordingly, academic dishonesty is prohibited in The City University of New York and at New York City College of Technology and is punishable by penalties, including failing grades, suspension, and expulsion. The complete text of the College policy on Academic Integrity may be found in the catalog.

Grading Procedure: Test1 15% Test2 15% Final Exam 25% Midterm 20% Projects 15% Homework 10% ===== TOTAL 100%

Letter A A- B+ B B- C+ C D F Grade Numerical 93- 90- 87- 83- 80- 77- 70- 60- <=59.9 Grade 100 92.9 89.9 86.9 82.9 79.9 76.9 69.9

Course Outline:

Week Topics Assignments and Readings

Week 1 Introduction Reading: CH 1 HW: CH 1.1,6,7,8,9 Supervised Learning Reading: CH 2 HW: CH 2.1, 2, 4, 5, 7, 8 Week 2 Bayesian Decision Theory Reading: CH 3.1-3.5, 8.1-8.5 Nonparametric Methods HW: CH 3.1, 2, 5; 8.2, 3, 4 Decision Trees Reading: CH 9, 19.1, 19.5-19.7 HW: CH 9.1, 2, 4 Assignment: Project1 Week 3 Linear Discrimination Reading: CH 10, 11.1-11.8.2 Multilayer Perceptrons HW: 10.1,7-9; 11.1-3 Week 4 Exam 1 Reading: CH 1-4, 8-11 Due: Project1 Exam 1 and Project 1 Review Week 5 Parametric & Multivariate Reading: CH 4, 5 Dimensionality Reduction Reading: CH 6, PCA Primer Week 6 Clustering Reading: CH 7 Week 7 Kernel Machines Reading: CH 13 Assignment: Project 2 Week 8 Combining Learners Reading: CH 17

Week 9 Exam 2 Review Session Exam 2 Reading: CH 4-7, 12-13, 17 Week 10 Reinforcement Learning Reading: CH 18.1-18.4 Due: Project 2 Week 11 Reinforcement Learning Reading: CH 18 all Assignment: Project 3 Week 12 Hidden Markov Models Reading: CH 15 Course Review Due: Project3 Week 13 Week 14 Research Paper Presentations Reading: CH 15-16, 18 Week 15 Finals

ASSESSMENT CRITERIA:

COURSE INTENDED LEARNING OUTCOMES/ASSESSMENT METHODS LEARNING OUTCOMES ASSESSMENT METHODS 1. Understand Bayesian Decision Theory and 1. Group activities, written report. Nonparametric Methods 2. Implement a supervised learning technique 2. Individual oral presentations, in‐class such as Decision trees on real world dataset group activities. 3. Understand Linear Discrimination and 3. Classroom discussion, in‐class Multilayer Perceptrons estimation group assignments. 4. Understand Reinforcement Learning and 4. Classroom discussion, in‐class group Hidden Markov Models assignments, learning logs. 5. Demonstrate an understanding of 5. Individual short essay related to dimensionality reduction using principal functions. components analysis 6. Demonstrate an understanding of the structure 6. Written report and group of neural networks and their use to solve presentation, learning logs. problems 7. Demonstrate an ability to write research 7. Written research paper articles which includes sections the following sections: Introduction, Related Work, Methodology, Empirical evidence and Conclusions

GENERAL EDUCATION LEARNING OUTCOMES/ASSESSMENT METHODS LEARNING OUTCOMES ASSESSMENT METHODS 1. Demonstrate the ability to work 1. Classroom discussions, group assignments and collaboratively and independently on individual oral presentations. assignments in and outside a classroom setting 2. Understand and employ both quantitative 2. Classroom discussion, group activities, group and qualitative analysis to solve problems. presentations, quizzes, tests, final exam.

3. Develop reading, writing competencies, 3. Biweekly reading and writing assignments, and listening skills. individual and group presentation, classroom discussion. Each homework assignment requires written 4. Work with teams. Build consensus. Use 4. Group projects and presentations. creativity.

Bibliography:

● Hastie, Friedman, and Tibshirani, The Elements of Statistical Learning, 2001 ● Bishop, Pattern Recognition and Machine Learning, 2006 ● Ripley, Pattern Recognition and Neural Networks, 1996 ● Duda, Hart, and Stork, Pattern Classification, 2nd Ed., 2002 ● Tan, Steinbach, and Kumar, Introduction to Data Mining, Addison-Wesley, 2005. ● Scholkopf and Smola, Learning with Kernels, 2002 ● Mardia, Kent, and Bibby, Multivariate Analysis, 1979 ● Computational Statistics (online book) ● Sutton and Barto, Reinforcement Learning: An Introduction, MIT Press, 1998. ● Bertsekas and Tsitsiklis, Neuro-Dynamic Programming, Athena Scientific, 1996.

B.2.7: CST4802 Information Retrieval 3 credits ( 2 lecture hours and 2 lab hours)

COURSE DESCRIPTION: The course details basic and advanced concepts and principles of information retrieval including: definition, framework, uses and ethical issues. Issues related to interface, relevance, language processing, and indexing are discussed. Information retrieval techniques covering both effectiveness and run-time performance are covered as well as heuristics and algorithms used in information retrieval systems. Information retrieval mechanism used in both Websites and Web search engines are also discussed. Ethical issues including personal privacy, bias, informed consent, monitoring and surveillance are also examined and discussed.

PREREQUISITES Student must have successfully completed CST4702 (Machine Learning Fundamentals)

LEARNING OUTCOMES: Upon successful completion of this course, the students have acquired knowledge and skills to: 1. Define and analyze tasks associated with information retrieval, web search and clustering 2. Define and analyze information retrieval strategies and models, and demonstrate an understanding of the issues and challenges related to information retrieval. 3. Develop strategies suited for specific retrieval, clustering and classification scenarios and recognize the limits of these strategies 4. Design and implement efficient and effective queries within Information Retrieval systems. 5. Analyze and utilize techniques used for relevance feedback. 6. Analyze and design an interface for information retrieval systems. 7. Develop and utilize knowledge organization methods including clusters, indexes and facets. Analyze these knowledge organization methods for their impact on retrieval. 8. Analyze and understand the issues and challenges related to non-text retrieval. 9. Analyze and understand the ethical issues related to the process of information retrieval.

GENERAL EDUCATION OUTCOMES: ● SKILLS/Inquiry/Analysis: Students use scientific reasoning and logical thinking. ● SKILLS/Communication: Students use written (both reading and writing), oral (both speaking and listening), and visual means to communicate. ● VALUES, ETHICS, RELATIONSHIPS / Professional/Personal Development: Students work in diverse teams utilizing key traits including respect, cooperation and creativity.

REQUIRED TEXTBOOKS Baeza-Yates, Ricardo and Berthier Ribeiro-Neto. Modern Information Retrieval the Concepts and Technology Behind Search 2nd Ed. New York: Pearson Education, 2011. ISBN-13: 978-0321416919

Manning, Christopher D., Prabhakar Raghavan, & Hinrich Schütze. An Introduction to Information Retrieval. London: Cambridge University Press, 2009. Online: https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf

ADDITIONAL MATERIAL The instructor will identify several additional information resources during the semester including Internet resources, print material (handouts) and reference books.

GRADING PROCEDURE: Tests (3) 30% Final Exam 20% Labs 15% Assignments 30% Participation 5% ===== TOTAL 100%

Letter A A- B+ B B- C+ C D F Grade Numerical 93- 90- 87- 83- 80- 77- 70- 60- <=59.9 Grade 100 92.9 89.9 86.9 82.9 79.9 76.9 69.9

COURSE OUTLINE:

Week Topics Reading Assignments Tests 1 Introduction to Information Baeza-Yates (Ch. 1) Lab 1: Getting Retrieval (IR) domain and to started with IR ethical issues related to this domain. 2 Information Retrieval Models: Baeza-Yates (Ch. 3) Lab 2:IR models Classic, Set Theoretic, Manning (Ch. 1) Algebraic, Probabilistic 3 Retrieval Evaluation Methods Baeza-Yates (Ch. 4) Lab 3: Indexing and Experiments: Cranfield Manning (Ch. 8) and the Cranfield Paradigm, retrieval metrics, experiments user-base evaluation, reference collections 4 Relevance Feedback – Baeza-Yates (Ch. 5) Lab 4:Relevance Relevance, precision, Manning (Ch. 9) feedback (Implicit/Explicit) Test 1 5 Documents - Languages and Baeza-Yates (Ch. 6) Lab 5: Data about Properties: Metadata, Manning (Ch. 2) data document formats, markup Manning (Ch. 12) languages, text properties, document processing, organizing documents, text compression 6 Queries - Languages and Baeza-Yates (Ch. 7) Lab 6:Queries Properties: Query languages, Manning (Ch. 12) Assignment 1 query properties. 7 Text Classification: Baeza-Yates (Ch. 8) Lab 7: Google vs. Supervised/Unsupervised Manning (Ch. 13) Bing is there a algorithms, evaluation metrics difference in (Recall/Precision), organizing terms of classes precision/recall? Test 2 8 Indexing and Searching: Baeza-Yates (Ch. 9) Lab 8:Indexing Inverted indexes, sequential Manning (Ch. 4) searching, multi-dimensional Manning (Ch. 5) Assignment 2 indexing 9 Parallel and Distributed IR: Baeza-Yates (Ch. 10) Lab 9:Parallel & Data partitioning, parallel IR, Distributed cluster-based IR, distributed IR, federated search, peer-to- peer retrieval networks 10 User Interfaces For IR: How Baeza-Yates (Ch. 2) Lab 10:User people search, search Interface interfaces, visualization in search interfaces Assignment 3

11 Web Retrieval: Search engine Baeza-Yates (Ch. 11) Lab 11:SE architectures, search engine Manning (Ch. 19) ranking (HITS/PageRank), Manning (Ch. 21) Assignment 4 managing Web data, search engine user interaction, browsing, bias and privacy 12 Web Crawling: Architecture Baeza-Yates (Ch. 12) Lab 12: and implementation, Manning (Ch. 20) Dissecting a scheduling algorithms, spider: how do Test 3 evaluation, bias, informed web crawlers do consent, surveillance and it? monitoring 13 Structured Text Retrieval: Chapter 13 Lab 13: Relational Explicit/Implicit, Manning (Ch. 10) vs. NoSQL query static/dynamic, XML languages retrieval, query languages 14 Multimedia IR: Content-based Baeza-Yates (Ch. 14) Lab 14: image retrieval, audio/music, Multimedia IR video, segmentation, compression, MPEG 15 Ethics of Information Retrieval (bias, privacy, informed consent, monitoring, surveillance)

Review Final Exam

COURSE INTENDED LEARNING OUTCOMES/ASSESSMENT METHODS

LEARNING OUTCOMES ASSESSMENT METHODS 1. Demonstrate an understanding of the 1. Classroom lecture, in class and online technical and ethical issues and interactive group discussions, written challenges related to information assignments retrieval 2. Examine and analyze various types of 2. Classroom lecture, in class and online retrieval systems interactive group discussions, written assignments 3. Build and use categories, clusters and 3. Utilize various industry tools and other organization techniques to gain techniques to code organized first-hand experience with issues information elements concerning the organization of information in relation to information retrieval

4. Analyze and use mathematical 4. Utilize various techniques and methods and computer code related to information retrieval models to code relevance and feedback retrieval algorithms 5. Analyze, design and use interface 5. Utilize design models to generate design techniques varying efficient user interfaces 6. Analyze and understand issues and 6. Classroom lecture, in class and online challenges related to non-text retrieval interactive group discussions, written assignments

GENERAL EDUCATION LEARNING OUTCOMES/ASSESSMENT METHODS

LEARNING OUTCOMES ASSESSMENT METHODS 1. Demonstrate the ability to work collaboratively 1. Classroom discussions, and independently on assignments in and outside a group assignments and classroom setting. individual oral presentations.

2. Understand and employ both quantitative and 2. Classroom discussion, group qualitative analysis to solve problems. activities, group presentations, quizzes, tests, final exam.

4. Work with teams. Build consensus. 4. Group projects and presentations. Use creativity.

Bibliography

Chowdhury, G. G. Introduction to Modern Information Retrieval 3rd edition. London: Facet Publishing, 2011. ISBN-13: 978-1856046947 Chu, Heting. Information Retrieval for a Digital Age 2nd edition. New York: ASIS&T, 2010. ISBN-13:978-1573873932 Hearst, Marti. Search User Interfaces. New York: Cambridge University Press, 2009. ISBN-13: 978-0521113793 Manning, Christopher D., Hinrich Schutze, and Prabhakar Raghavan. Introduction to Information Retrieval. New York: Cambridge University Press, 2008. ISBN-13: 978- 0521865715 Ouzzani, Mourad, Athaman Bouquettaya. Semantic Web services for Web Database. New York: Springer, 2011. ISBN-13: 978-1461416432

B.2.8: CST4812 Natural Language Processing 3 credits (2 lecture hours and 2 lab hours)

Course Description: This course introduces a broad range of techniques that aim to read, understand, and extract information from natural language text resources. The course covers both knowledge-based and statistical approaches for syntax, semantics, and discourse. In addition to introducing theoretical aspects and methods of natural language processing (NLP), the course covers multiple applications such as Machine Translation, Summarization, Interactive Dialog Systems, etc.

Pre-requisites: CST3512 (Information and Data Management II) and MAT2572 (Probability and Mathematical Statistics I)

Learning Outcomes: 1. Demonstrate knowledge of the issues and challenges NLP. 2. Demonstrate knowledge of the elements of NLP techniques and applications. 3. Process text through the language levels using Python-based libraries and software tools. 4. Work with NLP tools to create NLP applications. 5. Describe how NLP is used in many types of real world applications.

Required Textbook: S. Bird, E. Klein, E. Loper. Analyzing Text with Natural Language Toolkit Free on-line version: http://www.nltk.org/book/

Required Textbooks: D. Jurafsky & James H. Martin. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Second Edition, 2009. ISBN-13: 978-0131873216

Additional Reading Materials:

Foundations of Statistical Natural Language Processing, by Chritopher Manning and Hinrich Schuetze

The instructor will identify several additional information resources during the semester including Internet resources, print material (handouts) and reference books.

Grading:

Homework assignments 25% Mid-term exam 20% Project 30% Final exam 25% ===== TOTAL 100%

Letter Grade A A- B+ B B- C+ C D F Numerical 93- 90- 87- 83- 80- 77- 70- 60- <=59.9 Grade 100 92.9 89.9 86.9 82.9 79.9 76.9 69.9

The grade distribution follows the information in the NYCCT Student Handbook (p.43).

During the course of the class you are required to follow the NYCCT Academic Integrity Standards described in the Student Handbook (pp.95 – 99)

NYCCT Student Handbook can be downloaded here: http://www.citytech.cuny.edu/current- student/docs/StudentHandbook.pdf.

COURSE OUTLINE:

Week Topics Assignments and Readings 1 Introduction Ch. 0 2 Regular expressions; Ch. 1, Ch. 2, Ch. 3, Computing with language: text and words; Ch. 4 Searching text Assignment 1 Lab: regular expressions 3 Language modelling, n-grams, tf, tf*idf counts Ch. 5 Lab: computing text frequencies 4 Text corpora; Ch. 3, Ch. 4 Accessing / generating text corpora 5 Text classification using n-grams with tf*idf counts as Ch. 6 features Assignment 2 Lab: examples of text classification 6 Part-Of-Speech tagging; Ch. 2 Lab: Using POS taggers 7 Using Part-of-Speech tags for classification Ch. 2, Ch. 6 8 Mid-term; Ch. 8 Analyzing sentence structure Lab: the basics of syntactic analysis 9 Grammars; Context Free Grammars (CFG) Syntactic Ch. 9 parsing; statistical parsing 10 Dependencies and Dependency Grammar Ch. 9 11 Building Linguistic features Assignment 4 Semantic Analysis; The representation of meaning 12 Word Sense Disambiguation (WSD) Ch. 10 13 Applications review: Information extraction, question- Ch. 11 answering, summarization, machine translation, dialogue systems 14 Review/Final 15 Project discussion and presentation

ASSESSMENT CRITERIA: For successful completion of this course the student should be able to: For the successful completion of this course a student Evaluation methods and criteria should be able to: 1. Demonstrate knowledge of the issues and challenges Exams, homework assignments, NLP. in-class lab exercises and project 2. Demonstrate knowledge of the elements of NLP techniques and applications. 3. Process text through the language levels using Python-based libraries and software tools. 4. Work with NLP tools to create NLP applications. 5. Describe how NLP is used in many types of real world applications.

GENERAL EDUCATION LEARNING OUTCOMES/ASSESSMENT METHODS

LEARNING OUTCOMES ASSESSMENT METHODS 1. Demonstrate the ability to work 1. Classroom discussions, group assignments and collaboratively and independently on individual oral presentations. assignments in and outside a classroom setting. 2. Understand and employ both quantitative and 2. Classroom discussion, group activities, group qualitative analysis to solve problems. presentations, quizzes, tests, final exam. 3. Develop reading, writing competencies, and 3. Biweekly reading and writing assignments, listening skills. individual and group presentation, classroom discussion. Each homework assignment requires writing. 4. Work with teams. Build consensus. Use 4. Group projects and presentations. creativity.

Bibliography: • S. Bird, E. Klein, E. Loper. Analyzing Text with Natural Language Toolkit Free on-line version: http://www.nltk.org/book/ • Dan Jurafsky & James H. Martin (2009). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition, Second Edition, 2009. Publisher: Prentice Hall; 2nd edition ISBN-13: 978-0131873216 • Chritopher Manning and Hinrich Schuetze (1999). Foundations of Statistical Natural Language Processing Publisher: The MIT Press; 1 edition (June 18, 1999) ISBN-13: 978-0262133609

Appendix C: Letters of Support C.1: Letters of support from CUNY community colleges BOROUGH OF MANHATTAN COMMUNITY COLLEGE

LaGuardia College

C.2: Letters of support from graduate programs in the NYC area CUNY Graduate Center (Computer Science Department)

C.3: Letters of support from industry CITIGROUP

ERNST & YOUNG

Appendix D: Articulation Agreement with Community Colleges Articulation with Borough of Manhattan Community College (BMCC)

THE CITY UNIVERSITY OF NEW YORK ARTICULATION AGREEMENT Between NEW YORK CITY COLLEGE OF TECHNOLOGY And BOROUGH of MANHATTAN COMMUNITY COLLEGE

A. SENDING AND RECEIVING INSTITUTIONS

Sending College: Borough of Manhattan Community College (BMCC) Department: Computer Information Systems Program: Computer Science Degree: Associate in Science (A.S.)

Receiving College: New York City College of Technology (NYCCT) Department: Computer Systems Technology Program: Data Science Degree: Bachelor of Science (B.S.)

B. ADMISSION REQUIREMENTS FOR SENIOR COLLEGE PROGRAM

• The AS degree and a minimum GPA of 2.50 • Grade of C or higher in credit-bearing major courses • Grade of C or higher in freshman composition, its equivalent, or a higher level English course

Students who earn an AS in Computer Science program at BMCC will be accepted into the BS in Data Science under the requirements in effect at the time of admission. To earn a baccalaureate degree, admitted students must earn a minimum of 60 credits of which 34 credits must be taken in residence and 17 in the major..

Students who wish to transfer but do not meet all of the above requirements or are unable to enroll within two years after graduation will receive admission consideration under our standard transfer credit policies.

Total transfer credits granted toward the Bachelor of Science: 60

Total additional credits required at NYCCT to complete Bachelor of Science: 60

Total credits required for the Bachelor of Science in Data Science: 120

C. REQUIREMENTS OF BMCC AS IN COMPUTER SCIENCE DEGREE TRANSFER CREDITS AWARDED

BMCC graduates who complete the Associate in Sciences degree (A.S.) in Computer Science will receive 60 credits toward the Bachelor of Science (B.S.) degree in Data Science at NYCCT. BMCC Associate in Science in Computer Science Degree Requirements Required Common Core English Composition 6 Mathematical & Quantitative Reasoning1 3 Life & Physical Sciences2 3 Total Required Common Core 12 Flexible Common Core Creative Expression3 3 World Culture & Global Issues 3 U.S. Experience in its Diversity 3 Individual & Society 3 Scientific World4 6 Total Flexible Core 18 Total Common Core 30 Curriculum Requirements CSC 111 Introduction to Programming 4 CSC 211 Advanced Programming Techniques 3 CSC 215 Fundamentals of Computer Systems 3 CSC 231 Discrete Structures and Applications to Computer Science 4 CSC 331 Data Structures 3 CSC 350 Software Development 3 MAT 302 Analytic Geometry and Calculus II 4 General Electives5 6 Total Curriculum Credits 30 Total Program Credits 60

1MAT301 is advised to be taken to satisfy the area of Mathematical & Quantitative Reasoning. 2PHY215 is advised to be taken to satisfy the area of Life & Physical Sciences. 3SPE100 is advised to be taken to satisfy the area of Creative Expression. 4CSC101 is advised to be taken to satisfy the area of Scientific World. 5Some general electives credits can be satisfied by STEM variants taken in the Common Core.

City Tech agrees to accept the following Borough of Manhattan Community College courses as the equivalent to City Tech courses offered in the Bachelor of Science in Data Science:

City Tech Courses: Borough of Manhattan Course: B.S. in Data Science Computer Science CST1100 CSC101 CST1101 CSC215 CST1201 CSC111 CST3513 CSC211 CST3650 CSC331 MAT1575 MAT302 MAT2440 CSC231

D. SENIOR COLLEGEUPPER DIVISION COURSE REMAINING FOR BACCALAUREATE DEGREE Courses students will be required to take at NYCCT after completing AS in Computer Science to earn the BS in Data Science COLLEGE OPTION REQUIREMENTS Public Speaking COM 1330 or higher. If public speaking already taken, then as 3 advanced liberal arts course Interdisciplinary Any approved interdisciplinary (ID) course 3 Course Total Common Core & College Option Requirements 6

DISCIPLINE REQUIREMENTS MAT2575 Probability and Statistics I 4 MAT2580 Introduction to Linear Algebra 3 CST1204 Database Fundamentals 3 CST2302 Data Management I 3 CST2309 Web Programming I 3 CST2402 Introduction to Data Science 3 CST2410 Introduction to Security 3 CST3502 Data Management II 3 CST3512 Data Mining 3 CST3602 Data Visualization 3 CST4702 Machine Learning 3 CST4802 Information Retrieval 3 CST4812 Natural Language Processing 3 CST4900 Internship 3 Sub Total 43 Two Electives from the following BUS2339 Financial Management 3 BUS2341 Financial Forecasting 3 MED2400 Medical Informatics Fundamentals 3 MED4229 Healthcare Databases 3 BMET4741 Fundamental Healthcare Data Analytics 3 BMET4842 Advanced Healthcare Data Analytics 3

ECON1101 Macroeconomics 3 ECON2301 Money and Banking 3 MAT3672 Probability and Statistics II 3 MAT4672 Computational Statistics with Applications 4 CET4925 Internet of Things 3 CET4973 Introduction to Artificial Intelligence 3 PHYS3600 Machine Learning for Physics Astronomy 3

Sub Total 6/7 Electives to complete a minimum of 60 liberal arts credits and 4-5 120 total credits

Total 60 Writing Intensive Requirement Students at New York City College of Technology must complete two courses designated WI for the baccalaureate level, one from liberal arts and one from the major.

Total degree credits to be taken at NYCCT 60 Total Credits for Degree: 120

E. ARTICULATION AGREEMENT FOLLOW-UP PROCEDURE

In order to facilitate the efficient transition between our institutions, interested BMCC students are invited to utilize the pre-transfer advisement services of City Tech. Such services may be performed at NYCCT, or, by pre-arrangement, on-site at BMCC. Successful graduates are also assured of availability to all ancillary services at NYCCT.

1. Procedures for reviewing, updating, modifying or terminating agreement: When either of the degree programs involved in this agreement undergoes a change, the agreement will be reviewed and revised accordingly by faculty from each institution’s respective departments or programs, selected by their Chairpersons and program directors.

2. Procedures for evaluating agreement (i.e., tracking the number of students who transfer under the articulation agreement and their success): Each year New York City College of Technology (City Tech) will provide Borough of Manhattan Community College (BMCC) the following information: a) the number of BMCC graduates who applied to the program; b) the number of BMCC students who were accepted into the program; c) and the number of BMCC students who enrolled; d) the aggregate GPA of these enrolled students at City Tech.

3. Sending and receiving college procedures for publicizing agreement (e.g., college catalogs, transfer advisers, websites, etc.): • This articulation agreement will be publicized on the Borough of Manhattan Community College’s website, and New York City College of Technology website. • Transfer advisors at BMCC will promote this agreement with eligible students.

Both parties agree to notify each other of any changes in their respective programs. Such changes will commence discussions to as to how best to amend this agreement.

All the preceding agreements and commitments are herein certified as binding upon both BMCC and NYCCT until declared terminated by either party.

F. ADDITIONAL INFORMATION

Effective date: Spring 2020

For Borough of Manhattan For New York City College of Technology Community College

______Erwin Wong Bonne August Acting Provost Provost and Vice President of of Academic Affairs Academic Affairs Date:___/___/______Date:___/___/______

______Ching-Song Don Wei Hong Li Chairperson, Computer Information Chairperson, Computer Systems Systems Department Technology Department Date:___/___/______Date:___/___/______

Appendix E: Employment Status and Sample Employment Opportunities

1. Data Engineer (https://www.glassdoor.com/job-listing/data-engineer-mount-sinai-health- system-JV_IC1132348_KO0,13_KE14,39.htm?jl=2546746975) Mount Sinai Health System - New York, NY 10019

Job Summary

The Mount Sinai Health System

Do you have what it takes to wear the badge?

The Mount Sinai Health System’s commitment to excellence extends beyond delivering world- class health care. The System’s ongoing success is dependent upon our highly motivated, nonclinical professionals working to improve business operations. Our leadership team is driven to provide exceptional service by cultivating a workforce that is dedicated to upholding Mount Sinai’s mission of delivering innovative, breakthrough medicine with compassion and integrity.

Are you ready to discover the world of limitless possibilities that comes with wearing the badge? Explore more about this opportunity and how you can help us write a new chapter in our story of unrivaled patient care!

What You’ll Do:

The Data Engineer II focuses on data collection, movement, storage, transformation processing, and storage of Big Data. This individual works with both current ETL/Data Warehousing and future Big Data/Streaming/Pipeline architectures. The focus is on choosing optimal solutions to use for these purposes, then implementing, maintaining, and monitoring them, always being mindful of the overarching goal of accelerating translational research and improving clinical care.

• Facilitates data collection from a variety of sources, getting it in the right formats, assuring that it adheres to data quality standards, and assuring that downstream users can get that data quickly and with a common standard interface. • Ensures that data streams/pipelines are scalable, repeatable, and secure, and can serve multiple users within the Institute. • Develops as a core member of an Agile team, using Agile tools and methodology. Work closely with other team members including Application Developers, Database Developers, and Data Scientists. • Responsible for creating the infrastructure that provides insight from raw data and handles diverse sources of data seamlessly. • Enables big data and batch/real-time analytical solutions that leverage emerging technologies. • Additional responsibilities include developing prototypes and proof of concepts for the selected solutions, and implementing complex big data projects with a focus on

collecting, parsing, and managing large sets of data using multiple platforms to allow for Research and Data Science initiatives. • Translates business requirements into modern data pipeline solutions. Create centralized documents and diagrams of all solutions. • Creates a data catalog store of all metadata. • Designs and implements monitoring, backup, and disaster recovery of data systems. • Approaches all relationships with a world-class customer service approach. Maintains a customer-focused approach with users to provide solutions that are science/research- driven. • Responsible for the integrity and security of data in all forms of storage throughout the Data Architecture. • Works with other IT professionals through Mount Sinai effectively. Comply with the Institutional Review Board and HIPAA to follow all applicable policies and procedures. • Assists in the development of standards and procedures affecting data management, design and maintenance. Documents all standards and procedures. • Provides presentations and training to other team members in the above. • Possesses an extremely flexible attitude. Willing to work with multiple types of technologies and languages with an open mind and without technology bias. Continuous interest in updating skill sets and knowledge of trends in the Big Data Technology space. • Other duties as assigned.

What You’ll Bring:

Bachelor’s degree in Computer Science or a related discipline; Advanced degree preferred

• 4+ years relevant professional development experience, preferably in a LINUX environment. • Strong SQL and NoSQL Database Knowledge: Oracle, PostgreSQL/MYSQL, and Mongo DB (or similar). • Proficiency with at least 2 programming languages among Scala/Python/ Java. Must be flexible and fast to pick up new languages. • Proficiency in Restful service development, preferably with Node JS, Django and PHP. • Experience with micro-services and SOA. • Strong SQL and NoSQL Database Knowledge: Oracle, PostgreSQL/MYSQL, and Mongo DB (or similar). • Proficiency on installation and configuration of big data software and technology • Knowledge of Hadoop, Spark, Kafka and other big data technology stacks and streaming tools. • Familiarity with and the ability to leverage a wide variety of open source technologies and tools. • Working knowledge of cloud architecture and implementation on Azure or AWS, is a big plus. Experience with server-less computing, creating VMs, cloud security, and other cloud services is also a big plus. • Experience working in an Agile methodology. Experience working with JIRA is a plus.

Who We Are:

Over 35,000 employees strong, the mission of the Mount Sinai Health System is to provide compassionate patient care with seamless coordination and to advance medicine through unrivaled education, research, and outreach in the many diverse communities we serve.

Formed in September 2013, The Mount Sinai Health System combines the excellence of the Icahn School of Medicine at Mount Sinai with seven premier hospital campuses, including Mount Sinai Beth Israel, Mount Sinai Beth Israel Brooklyn, The Mount Sinai Hospital, Mount Sinai Queens, Mount Sinai Roosevelt, Mount Sinai St. Luke’s, and New York Eye and Ear Infirmary of Mount Sinai.

The Mount Sinai Health System is committed to the tenets of diversity and workforce that are strengthened by the inclusion of and respect for our differences. We offer our employees a highly competitive compensation and benefits package, a 403(b) savings plan, and much more.

The Mount Sinai Health System is an equal opportunity employer. We promote recognition and respect for individual and cultural differences, and we work to make our employees feel valued and appreciated, whatever their race, gender, background, or sexual orientation.

EOE Minorities/Women/Disabled/Veterans

2. Big Data Consultant (https://www.amazon.jobs/en/jobs/554290/big-data-consultant)

Job ID 554290 Location US-NY-New York Posted Date 7/5/2017 Company Amazon Web Services, Inc.

Job Description At Amazon Web Services (AWS), we’re hiring highly technical cloud computing architects to help our partners develop technical expertise and capacity, while also collaborating with our customers and partners on key engagements. Our consultants will deliver proof-of-concept projects, topical workshops, and lead implementation projects. These professional services engagements will focus on key customer solutions such as, web applications, enterprise applications, HPC, batch processing and big data, archiving and disaster recovery, education and government. Professional Services engage in a wide variety of projects for customers and partners, providing collective experience from across the AWS customer base and are obsessed about strong success for the Customer. Our team collaborates across the entire AWS organization to bring access to product and service teams, to get the right solution delivered and drive feature innovation based upon customer needs. Responsibilities include:

• Expertise - Collaborate with AWS field sales, pre-sales, training and support teams to help partners and customers learn and use AWS services such as Amazon Elastic Compute Cloud (EC2), Amazon Data Pipeline, S3, DynamoDB NoSQL, Relational Database Service (RDS), Elastic Map Reduce (EMR) and Amazon Redshift. • Solutions - Deliver on-site technical engagements with partners and customers. This includes participating in pre-sales on-site visits, understanding customer requirements, creating consulting proposals and creating packaged Big Data service offerings. • Delivery - Engagements include short on-site projects proving the use of AWS services to support new distributed computing solutions that often span private cloud and public cloud services. Engagements will include migration of existing applications and development of new applications using AWS cloud services.

Basic Qualifications • BA/BS degree or equivalent experience; Computer Science or Math background preferred • 5+ years’ experience of IT platform implementation in a highly technical and analytical role.

• Customer facing skills to represent AWS well within the customer’s environment and drive discussions with senior personnel regarding trade-offs, best practices, project management and risk mitigation • Demonstrated ability to think strategically about business, product, and technical challenges in an enterprise environment. • Deep understanding of database and analytical technologies in the industry including MPP databases, noSQL storage, Data Warehouse design, BI reporting and Dashboard development. • Implementation and tuning experience for Apache Hadoop + tools such as Pig and Hive. • Current hands-on implementation experience required; individual contributors only need apply. • Strong verbal and written communications skills and ability to lead effectively across organizations. • Ability to travel to client locations when needed. Preferred Qualifications • Hands on experience leading large-scale global data warehousing and analytics projects. • Demonstrated industry leadership in the fields of database, data warehousing or data sciences. • Implementation and tuning experience specifically using Amazon Elastic Map Reduce + tools such as Pig and Hive. • Track record of implementing AWS services in a variety of distributed computing, enterprise environments.

Amazon is an Equal Opportunity-Affirmative Action Employer-inority/Female/Disability/Vet

3. Database Analyst (https://www.nespressojobs.com/job/new-york/database- analyst/5736/5761202)

Job location: New York, New York Position type: Professional Req No: 17006809 Our story began over 25 years ago with a simple but revolutionary idea – to create the perfect cup of coffee. An industry pioneer, Nestlé Nespresso has become an international reference for the highest quality coffee and an iconic symbol of refined elegance. We are now in more than 50 countries and our team has grown well beyond 7,000 employees. We are part of Nestlé SA in Switzerland, the world's largest food company. Nespresso USA continues to drive momentum and innovation in our market segment, and in North America, we're just getting started.

Nestle Nespresso USA is searching for a driven, detail oriented, Database Analyst to lead deep dive analyses and advanced analytics projects to support database/CRM activation. This role will work closely with the CRM Insights Manager to develop and execute analytics plans including: promotional dashboards, big picture business analyses, and other vendor driven projects.

Responsibilities • Act as primary point of contact for database related questions for internal and external stakeholders • Drive database and technological advancements to improve level and efficiency of analyses as well as database hygiene • Generate queries and in general leverage data to address key business questions • Mine data for insights and key trends that address business questions and inform strategic decisions • Deepen understanding of the Nespresso consumer through customer level analysis • Drive omni channel strategy through holistic, cross-channel analyses • Communicate key insights and recommendations to stakeholders • Determine where predictive and attribution modeling will best enhance the businesses core competencies • Work with CRM Insights Manager to develop a deployment plan and implementation of the model • Review business objectives and key trends with CRM Insights Manager and other CRM counterparts to develop predictive model-based segmentation strategies • Create personalized communication strategy that increases consumers’ engagement with the brand and lifetime value

Requirements • Bachelor’s Degree required, preferably in business, marketing, statistics, or computer science • 4+ years of experience in an analytics focused role • 2+ years of experience in direct to consumer marketing analytics • Advanced SQL querying skills required • Prior experience using data analysis tools required (SPSS, SAS, etc.) • Prior experience using business intelligence tools strongly preferred (Cognos, SAP Business Objects, etc.)

• Prior experience using data visualization tools preferred (Tableau, Power BI, etc.) • Strong analytical skills • Ability to clearly deliver insights and findings • Ability to integrate multiple data sources into a single narrative • Strong strategic thinking skills • Strong presentation skills

The Nestle companies are equal opportunity and affirmative action employers and are looking for diversity in candidates for employment: Minority/Female/Disabled/Protected Veteran

4. Data Scientist (https://www.glassdoor.com/job-listing/data-scientist-bankers-healthcare- group-JV_IC1132348_KO0,14_KE15,39.htm?jl=2625375731)

Bankers Healthcare Group 20 reviews - New York, NY Job Summary

We are seeking a talented data scientist to work on revenue predictive analytics team at BHG.

You will provide advanced analytics and predictive modeling skills to drive business decision making. Utilize data mining techniques to understand revenue life circle of BHG customers. Automate complicated works and create efficiency.

You will be deeply involved in evaluation of new data source, and extracting, cleaning, modeling of the data and experimental design.

Requirement:

• 2-5 years of professional experience working as a Data Scientist • Master's degree, or PhD in Computer Science, Statistics, Mathematics, Engineering, Econometrics, or related fields from an accredited college or university • Proficient in statistics and machine learning, including exploratory data analysis, regression, classification, experimental design and clustering. • Proficient in extracting, cleaning, and modeling of the data. • 2-5 years of professional Python or R experience • 2-5 years of professional SQL experience • Detailed oriented and work well under pressure • Good communication skills and team player • Ideally 2 plus years marketing data science experience

Job Type: Full-time

Required education:

• Bachelor's Required experience:

• Machine Learning: 2 years • R: 1 year • SQL: 2 years • Predictive Modeling: 2 years

5. Data Analyst (https://www.glassdoor.com/job-listing/data-analyst-popular-community-bank- JV_IC1132348_KO0,12_KE13,35.htm?jl=2626947239) Apply Now Date: Jan 3, 2018 Location: New York, NY, US, 10004 Join the Popular Team!

Founded in 1893, Popular Inc. has been built upon strong institutional values while providing broad financial services within the United States, Puerto Rico, and the Caribbean.

We strive to create an extraordinary legacy with a passion for customer service, tremendous dedication to our employees and strong partnerships in the communities where we reside. Come explore Popular Community Bank and see why we are “The Human Side of Banking.”

Strategic Planning

We are seeking a Data Analyst who can combine strong analytic capabilities, database skills, and communication to help shape our reporting and analytical needs. The Analyst will need to be able to drive forward internal projects with a combination of business stakeholders, technical stakeholders, and support teams by being the bridge between business and systems. The Analyst will play an integral part in creating and enhancing business intelligence systems and frameworks. Please note that this position will be more technical and would be ideal to applicants who wish to begin or further their experience with coding, practice, and development of BI systems with financial institutions.

In this position, you will:

• Develop, analyze, and evaluate data to create and maintain business intelligence frameworks that supports the Bank’s strategic objectives; • Demonstrate a sufficient understanding of BI systems and engage and communicate effectively both to non-technical personnel, as well as internal developers regarding regular business and QA requirements; • Work with internal and external IT partners to ensure data feeds are accurate and delivered in a timely manner; • Analyze and assess business problems and processes by applying conceptual knowledge and discretion to independently define functional and non-functional requirements for technology solutions, consult and make recommendations accordingly • Maintain/update/create documentation of knowledge assets, e.g. business rules, technical specifications (data mapping, data flows, dashboard content, data dictionaries, relational diagrams, QA plans, etc.) • Administer security for various applications • Responsible for the timely resolution of various Compliance and Audit items.

To be considered, you will need:

• Bachelor’s degree required, preferred in Information Systems, Computer Science, or other technical background • Minimum of 2-3 years in a similar position, or experience in data mining, date a management, financial analysis or other related fields in the financial services industry • Knowledge of business intelligence applications and/or statistical analysis software, e.g., Microsoft Office (Excel VBA), SAS, Tableau, SQL, Power Pivot, Business Objects Desktop/Web Intelligence • Familiarity with the IBM Cognos TM1 platform is preferred, but not necessarily required • Understand concepts of database structures, data querying, ETL, data mining, data auditing and technical documentation is preferred • The ability to deal with and resolve conflicting and competing demands, prioritize and organize work to meet deadlines and collaborate with a variety of stakeholders with little supervision • Flexibility with general ambiguity as it refers to stakeholder requirements, needs, and daily work as this position interfaces with many departments and stakeholders

6. Data Scientist, Analytics (New York, NY) Facebook's mission is to give people the power to build community and bring the world closer together. Through our family of apps and services, we're building a different kind of company that connects billions of people around the world, gives them ways to share what matters most to them, and helps bring people closer together. Whether we're creating new products or helping a small business expand its reach, people at Facebook are builders at heart. Our global teams are constantly iterating, solving problems, and working together to empower people around the world to build community and connect in meaningful ways. Together, we can help people build stronger communities — we're just getting started.

We’re looking for Data Scientists to work on our core and business products (Instagram, Ads, Messaging, Identity, Growth & Engagement, Mobile, Search, Privacy, Payments) with a passion for Internet technology to help drive informed business decisions for Facebook. You will enjoy working with one of the richest data sets in the world, cutting edge technology, and the ability to see your insights turned into real products on a regular basis. The perfect candidate will have a background in a quantitative or technical field, will have experience working with large data sets, and will have some experience in data-driven decision making. You are scrappy, focused on results, a self-starter, and have demonstrated success in using analytics to drive the understanding, growth, and success of a product. This position is located in our New York City office.

Responsibilities • Apply your expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with both our consumer and business products • Partner with Product and Engineering teams to solve problems and identify trends and opportunities • Inform, influence, support, and execute our product decisions and product launches • The Data Scientist Analytics role has work across the following four areas: • Product Operations • Forecasting and setting product team goals • Designing and evaluating experiments • Monitoring key product metrics, understanding root causes of changes in metrics • Building and analyzing dashboards and reports • Building key data sets to empower operational and exploratory analysis • Evaluating and defining metrics • Exploratory Analysis • Proposing what to build in the next roadmap • Understanding ecosystems, user behaviors, and long-term trends • Identifying new levers to help move key metrics • Building models of user behaviors for analysis or to power production systems • Product Leadership • Influencing product teams through presentation of data-based recommendations • Communicating state of business, experiment results, etc. to product teams • Spreading best practices to analytics and product teams

• Data Infrastructure • Working in Hadoop and Hive primarily, sometimes MySQL, Oracle, and Vertica • Automating analyses and authoring pipelines via SQL and Python based ETL framework

Minimum Qualifications • 5+ years of experience doing quantitative analysis. • BA/BS in Computer Science, Math, Physics, Engineering, Statistics or other technical field. Advanced degrees. • Experience in SQL or other programming languages. • Development experience in any scripting language (PHP, Python, Perl, etc.) • Ability to communicate the results of analyses. • Understanding of statistics (e.g., hypothesis testing, regressions). • Experience manipulating data sets through statistical software (ex. R, SAS) or other methods.

Preferred Qualifications • Experience with distributed computing (Hive/Hadoop)

7. Data Science Analyst I - (Mount Sinai Hospital) Day Shift (https://www.glassdoor.com/job-listing/data-science-analyst-i-mount-sinai-hospital-day-shift- mount-sinai-queens-JV_IC1132348_KO0,53_KE54,72.htm?jl=2610877506) The Mount Sinai Hospital The Mount Sinai Health System Do you have what it takes to wear the badge?

The Mount Sinai Health System's commitment to excellence extends beyond delivering world- class health care. The System's ongoing success is dependent upon our highly motivated, nonclinical professionals working to improve business operations. Our leadership team is driven to provide exceptional service by cultivating a workforce that is dedicated to upholding Mount Sinai's mission of delivering innovative, breakthrough medicine with compassion and integrity.

What You'll Do:

The Data Science Analyst for Access/Transfer Center collaborates with stakeholders from across the organization to develop sophisticated analytics to provide information, insights and BI (Business Intelligence) solutions that contribute to sound strategic planning, decision-making, goal setting, and effective performance measurement. This individual demonstrates sound understanding of the healthcare domain, technical data manipulation and analytic development skills and impact the patient community of the Mount Sinai Health System. This analyst will be working at Mount Sinai Corporate Office near Grand Central.

Duties and Responsibilities: Analyzes data requests for Access/Transfer Center using information technology, enrollment, claims, clinical, contract, medical management, financial, administrative and other corporate data from both modeled and disparate internal and external sources. 1. Works with departmental staff to identify requirements for reporting and / or business intelligence tools. 2. Identifies necessary data, data sources and methodologies. 3. Collects, organizes, integrates, analyzes and interprets data. 4. Leverages advanced statistical analysis methods to create insightful recommendations and conclusions that may be communicated to the stakeholder. 5. Identifies and addresses expected and unforeseen data complexities to mitigate their impact on the analytic outcome and associated business decisions. Works to improve data quality where possible within created analytical models. Feeds data quality issues back to IT or identified data stewards to facilitate creation of high quality metrics. 6. Develops and may present reports, analyses and findings to senior management and others as scheduled or requested.

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Responsible for one or more of the following stakeholder groups: 1. Contracting and Commercialization – May assist in the modeling and forecasting contract scenarios, measuring ongoing performance and identify trends in performance to inform our clinical or contracting staff to improve contract outcomes. 2. Care Management – Helps to identify, understand and prioritize at-risk members in need of care management. Helps stratify our membership to optimally use resources to focus on the patients most in need, currently or in the future. 3. Medical Directors – Helps to identify utilization trends and variations across the different categories of health care services to assist the Medical Directors to focus their efforts to maximize contract performance and clinical effectiveness. 4. Quality and Documentation – Helps to link payer quality and documentation opportunities into operational analytic processes to maximize our quality scores, top line revenue and optimize the use of resources in concert with MS Health System contracts. 5. I.T. / High Performance Computing in any ongoing projects. 6. Acts as liaison/analyst for internal stakeholders, understands their needs and translates them into reporting and analytic solutions. 7. Effectively communicates with stakeholders and customers and ensures all requests are properly triaged, recorded and tracked. 8. Adheres to corporate standards for performance metrics, data collection, data integrity, query design, and reporting format to ensure high quality, meaningful analytic output. 9. Helps identify and understand data from internal and external sources for competitive, scenario and performance analyses, and financial modeling to gain member/provider insight into new and existing processes and business opportunities. 10. Works closely with IT on the ongoing improvement of Mount Sinai's integrated data warehouse, driven by strategic and business needs, and designed to ensure data and reporting consistency throughout the organization.

Other duties assigned as per Director. Education: BA or BS degree minimum, in a relevant field of study; Master’s degree preferred. Experience: • years minimum in analytics development expertise, preferably in health care, or for a health provider, health plan or accountable care organization, including either: • Working knowledge of a health care EMR such as Epic/Clarity, aCW, etc.; a payor claims system such as Facets, Amisys, etc.; or a hospital/provider system such as IDX, Soarian, etc. • Knowledge of the New York State Medicaid and CMS Medicare regulations and related reporting requirements, such as STARS, QARR, MMCOR, MEDS, RAPS and HEDIS is a strong plus. • Experience working in a health plan or consulting actuarial, financial reporting or medical economics departments highly valuable. • Experience working in healthcare provider analytics related to revenue modeling, managed care contracting, population management, case management, clinical or financial decision report.

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Appendix F: General Information Institution (Legal Name) Institution Code

New York City College of Technology 333800

Proposed Program Title Degree Award

Data Science Bachelor of Science

Full-time or Part- Address of Any Campus Where the Proposed Program Will Be Offered time 1 (main and/or branch campuses)

300 Jay St, Brooklyn, NY 11201 Full-time

All Program Format(s) (standard, distance education2, evening, weekend HEGIS Code and/or other)

Standard, evening and weekend 5101.00

Total Number of Joint Registration IHE (if applicable) Credits

N/A N/A

Lead Contact [First Name, Last Name, Title] Telephone Number

Hong, Li, Professor and Chair 718-260-5170

Email Address

[email protected]

1 Please refer to §52.2(c) and §145-2.1 of the Regulations of the Commissioner for definitions and information concerning full and part time study. Note: Only programs registered as full time are eligible for TAP. Programs are subject to audit by the NYS Office of the State Comptroller and the Higher Education Services Corporation (HESC) for financial aid compliance purposes. 2 If a major portion of the program (50% or more) can be completed through study delivered by distance education then the program must be registered in the distance education format. Hybrid or blended courses do not count toward the 50%.

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Appendix G: Program Purpose, Objectives and Targets Program Purpose Department Expectation: Clearly define a program purpose that is aligned to the degree award and program title. The purpose and goal of the Bachelor of Science (BS) in Data Science program is to prepare graduates with the technical skills necessary to enter careers in the Data Science field, which is one of the fastest- growing fields today. This program would be a synthesis of applied mathematics, high-performance computing, data management and analysis, which is in high demand, and does not currently exist at any of the CUNY colleges. The BS in Data Science program will provide students with strong technical skills, critical thinking, and problem-solving abilities that are highly rated by companies in all fields, including but not limited to, Finance, Programming, Education, Medicine, Biology. Our graduates will be well equipped with a solid platform in mathematics, computing, and data management and analysis, and the program will provide pathways for employment after graduation, as well as for admission to graduate programs, including CUNY own MS in Data Science program in CUNY Graduate Center.

Program Objectives Department Expectation: Articulate between 1 and 3 program-level (curriculum-level) objectives that are clearly defined and directly aligned with the program purpose and proposed degree award. 1. Identify, analyze and use data science strategies and tools to solve business problems and improve business decision making. 2. Study principles and techniques for data collection, curation, analysis, mining. Demonstrate the ability to apply these techniques to real world problems.

3. Demonstrate an understanding of the ethical issues (e.g., privacy, accountability, no maleficence, fairness, etc) that arise from the process of mining user data.

Program Targets - Department Expectation: Establish realistic enrollment, retention, graduation, and job placement targets for this program that are connected to the reviewing system by which the success of students and faculty in achieving such goals and objectives of the program are determined. Note: There are not specific Department defined targets required for the registration of curricula. The Department expects institutions to establish targets that reflect the espoused quality of the program, and to periodically and systematically review such targets are they related to program implementation. Enrollment Projections

The Department assumes that Year 5 enrollment projections will be full-capacity relative to existing and new resources planned. Year 1 Year 2 Year 3 Year 4 Year 5

76 82 87 93 93

Annual Retention Rate Target (%) Target graduation rate (%) Target Job Placement Rate (%)

84% 67% 85%

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Appendix H: Curriculum and Course Information Please provide the following:

1. The applicable sample student program schedule table: • Table A: Undergraduate Program Schedule; or • Table B: Graduate Program Schedule

When completing the program schedule table please refer to the requirements in §52.2(c) of the Regulations of the Commissioner concerning completion of Associate, Baccalaureate and Master’s degree programs.

2. Please list the course titles for all new courses included as part of the proposed program, and, either attach the course syllabi or, if such syllabi are not yet available, provide course descriptions and objectives in the chart below.

Indicate that course syllabi are attached or, provide course New Course Titles descriptions and objectives (if course syllabi are not available)

CST2312 Information and Data Management I Page 34 of the current proposal

CST2402 Introduction to Data Science Page 39 of the current proposal

CST3502 Data Mining Page 44 of the current proposal

CST3512 Information and Data Management II Page 49 of the current proposal

CST3602 Data Visualization Page 54 of the current proposal

CST4702 Machine Learning Fundamentals Page 58 of the current proposal

CST4802 Information Retrieval Page 63 of the current proposal

CST4812 Natural Language Processing Page 69 of the current proposal

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Appendix I: Undergraduate Program Schedule . Indicate academic calendar type: Semester Quarter Trimester Other (describe): . Label each term in sequence, consistent with the institution’s academic calendar (e.g., Fall 1, Spring 1, Fall 2) . Use the table to show how a typical student may progress through the program; copy/expand the table as needed. Term: Fall 1 Credits per classification Term: Spring 1 Credits per classification Course Number & Course Number & Title Cr LAS Maj New Prerequisite(s) Title Cr LAS Maj New Prerequisite(s) CST 1201: CST 1101 and CST 1100: Introduction to 3 X CUNY Proficiency Programming 3 X (CST 1100 for Computer Systems Fundamentals CST students) CST 1101 and CST 1204: Database CST 1101: Problem Solving 3 X CUNY Proficiency 3 X (CST 1100 for Fundamentals CST students) Required Core: Quantitative 3 X MAT 1475: Calculus I 4 X X MAT 1375 Reasoning ENG 1101(Required Core): CUNY Read and Required Core: Life 3 X 3 X English Composition I Write Proficiency & Physical Sciences ENG 1121 (Required Flexible Core: World 3 X Core): English 3 X ENG 1101 Cultures Composition II Term credit total: 15 9 6 Term credit total: 16 10 9 Term: Fall 2 Credits per classification Term: Spring 2 Credits per classification Course Number & Course Number & Title Cr LAS Maj New Prerequisite(s) Title Cr LAS Maj New Prerequisite(s) CST 1204, CST CST 2312: Information and 3 CST 2402: Intro. to X X CST 1101 3 X X 1201, Pre or Co- Data Management I Data Science Req: MAT 1475 CST 2309: Web CST 2410: Intro. to 3 X CST 1201 3 X CST 2307 programming Computer Security MAT 1575: Calculus II MAT 2572: 4 X X MAT 1475 Probability and 4 X X MAT 1575 Statistics I MAT 2440: Discrete MAT 1375 or Flexible Core: US Structures and Algorithms I 3 X X higher, CST 1201 3 X Experience or CST 2403 Flexible Core: Any 3 X Term credit total: 16 10 13 3 Term credit total: 13 7 9 3

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Term: Fall 3 Credits per classification Term: Spring 3 Credits per classification Course Number & Title Course Number & Cr LAS Maj New Prerequisite(s) Title Cr LAS Maj New Prerequisite(s) CST 3512: Information and CST 3602: Data 3 X CST 3512 and 3 X X CST 2312 Data Management II Visualization MAT 2572 CST 3513: Object Oriented CST 3650: Data 3 X CST 3503 or CST 3 X CST 1201 Programming Structures 3513 CST 3502: Data Mining CST 2312, CST MAT 2580: Intro. to 3 X X MAT 1575 3 X X 2402 Linear Algebra Flexible Core: Scientific Flexible Core: 3 X 3 X World Creative Expression College Option: LLAA I College Option: 3 X 3 X LLAA II

Term credit total: 15 6 9 3 Term credit total: 15 9 9 Term: Fall 4 Credits per classification Term: Spring 4 Credits per classification Course Number & Course Number & Title Cr LAS Maj New Prerequisite(s) Title Cr LAS Maj New Prerequisite(s) Data Science Major 3 X Data Science Major 3 X Elective I Elective II CST 4702: Machine 3 X X CST 3650, CST CST 4802: 3 X X CST 4702 Learning Fundamentals 3502, MAT 2572 Information Retrieval CST 4714: Database 3 X CST 2405 or CST CST 4812: Natural 3 X X CST 3512, MAT Administration 2415 or CST 3604 Language 2572 Processing College Option: Speech 3 X CST 4900: Internship 3 X Flexible Core: Individual 3 X College Option: 3 X and Society Interdisciplinary

Term credit total: 15 6 9 3 Term credit total: 15 3 12 6

Program Credits: 120 Liberal Arts & Sciences: 60 Major: 76 Elective & Other: 6 Totals: Cr:= credits LAS = Liberal Arts and Sciences Maj = major requirement New = new course Prerequisite(s) = list prerequisite(s) for the noted courses

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Appendix J: Faculty Teaching Assignments Form Department Expectations: Identify the specific faculty members that will be responsible for setting the curricular objectives, teaching program courses, advising students, and determining the means by which program and course objectives are measured. Identify the program director. Core faculty members must meet minimum academic qualifications as identified in Part 52.2(b) of regulation, and be of sufficient depth and breadth to provide leadership, direction, and discharge other responsibilities critical to the start-up of the program. Note: Faculty curricula vitae or resumes should not be attached to this application and should only be provided if specifically requested by the Department

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Faculty Member Name, Title, Courses to be taught Full-time or Part-time; Highest Earned Additional qualifications which demonstrate professional and Rank if Full-time identify % Degree, competence relative to the specific program. of time to the program Discipline, IHE Note: For each faculty we list research interests and two recent relevant publications Candido Cabo, CST 1101 (Problem Full-Time, 40% Ph.D. in Dr. Cabo’s research interests include Professor Solving) Biomedical computational biology and bioinformatics, high CST 1201 Engineering, performance computing and computer science (Programming Duke education. Fundamentals) University Cabo C, Post-repolarization refractoriness CST 4702 (Machine increases vulnerability to block and initiation of Learning reentrant impulses in heterogeneous infarcted Fundamentals) myocardium, Comput. Biol. Med. 65: 209-219, 2015. Cabo, C., Satyanarayana, A. (2018) Promoting Students’ Social Interactions Results in an Improvement in Performance, Class Attendance and Retention in First Year Computer Building a Community of First Year Computing Courses. In 2018 IEEE Frontiers in Education (FIE) Conference. New York: IEEE, 2018, October 3-6, 2018, San Jose, CA. Yu-Wen Chen, Assistant CST 2410 Full-Time, 40% Ph.D. in Dr. Chen’s research interests include cloud Professor (Introduction to Computer computing, smart grid, information security, and Security) Engineering, big data optimization. Iowa State A. Satyanarayana, J.Kusyk and Y-W.Chen, University “Design of Cloud Based Robots using Big Data Analytics and Neuromorphic Computing”, 31st IEEE Canadian Conference on Electrical and Computer Engineering (CCECE 2018), Quebec, Canada, May 13-16th 2018 Y. W. Chen and J. M. Chang, "Fair Demand Response With Electric Vehicles for the Cloud Based Energy Management Service," in IEEE Transactions on Smart Grid, vol. 9, no. 1, pp. 458- 468, Jan. 2018

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Elena Filatova, CST 2312 Full-Time, 80% Ph.D. in Dr. Filatova’s research interests are in Natural Assistant Professor (Information and Computer Languages Processing and Text Mining. Data Management I) Science, E. Choban, E. Filatova, Incendiary News CST 3512 Columbia Detection, 2019. In the Proceedings of the 32nd (Information and University. International the Florida Artificial Intelligence Data Management II) Research Society conference, (FLAIRS-32), CST 3602 (Data Sarasota, FL. Visualization) D. Difallah, E. Filatova, P. Ipeirotis, Demographics CST 4812 (Natural and Dynamics of Mechanical Turk Workers, 2018. Language In the Proceedings of the 11th ACM International Processing) Conference on Web Search and Data Mining (WSDM), Los Angeles, CA

Hong Li, Professor CST 1101 (Problem Full-Time, 40% Ph.D. in Dr. Li’s research interests include mathematical Solving) Mathematics, modeling and artificial neural networks. CST 1201 University of Hong Li, Short-Term Forecasting of Wind Power (Programming Oklahoma Generation by Adaptive Neural Networks, Fundamentals) International Journal of Modern Engineering, Vol. CST3650 ( Data 17, Number 2, Spring/Summer 2017, pp.59-64 Structure) Hong Li, Ali Setoodehnia and Kamal Shahrabi, Model of Electric Power Load by Adaptive Neural Network, 2017 International Conference on Artificial Intelligence: Techniques and Applications, Sep. 16-18, 2017 Elizabeth Milonas, CST 2402 Full-Time, 80% Ph.D. in Dr. Milonas’ research interests focus on Assistant Professor (Introduction to Data Computer Knowledge Organization (KO) philosophy, Science) Information methods and techniques within the Web domain CST 4802 Science, Long and particularly in the result pages of Web Search (Information Island City Engines. Dr. Milonas has several publications Retrieval) University. related to KO and search engines. Milonas, Elizabeth. “An Examination of Facets Within the Search Engine Result Pages” In Dimensions of Knowledge: Facets for Knowledge Organization, edited by Richard P. Smiraglia and Hur-Li Lee, 87-113. Würzburg: Ergon Verlag, 2017.

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“Does Design Contribute to Ease of Use: An exploratory study of Web Facets in The Result Pages of Two Search Engines.” Information, Interaction, Innovation: Celebrating the Past, Constructing the Present and Creating the Future, Proceedings of the 75th ASIS&T Conference, October 26-30, 2012, Baltimore, Maryland. Ashwin Satyanarayana, CST 3502 (Data Full-Time, 80% Ph.D. in Dr. Satyanarayana’s research interests are in Associate Professor Mining) Computer building Data Mining tools and Big Data analytics. CST 4702 (Machine Science, He is an author or co-author of over 25 peer Learning SUNY-Albany reviewed journal and conference publications in Fundamentals) the area of Data Analysis and Data Science. Benakli, N., Kostadinov, B., Satyanarayana, A., & Singh, S. (2017). Introducing computational thinking through hands-on projects using R with applications to calculus, probability and data analysis. International Journal of Mathematical Education in Science and Technology, 48(3), 393- 427. Satyanarayana, A., & Nuckowski, M. (2016). Data mining using ensemble classifiers for improved prediction of student academic performance. ASEE Mid-Atlantic Section Spring 2016 Conference, George Washington University, Washington D.C, April 2016.

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Appendix K: Faculty to be Hired Department Expectations: Identify the specific job title, courses to be taught, and qualifications for each position and the specific timeline by which the faculty member(s) will be hired. The job descriptions and minimum qualifications of faculty to be hired meet the meet minimum academic qualifications as identified in Part 52.2(b) of Commissioner’s regulation. The date provided by which faculty to be hired will be in place must be clear and directly connected to when they are needed to discharge their responsibilities during program implementation. The Department reserves the right to request more information concerning recruitment and hiring of faculty if it is needed to make a determination concerning compliance with program registration standards.

Title/Rank of No. of Minimum F/T or Percent Expected Course Expected Position New Qualifications P/T Time to Assignments Hiring Date Positions Program

Assistant 2 Ph.D. Computer F/T 100% Data Science 9/1/19 Professor Science or Related Courses field Adjunct 10 Masters in P/T 100% Introductory Data 9/1/19 Professor Computer Science Science Courses or Related field

Appendix L: New Resources, Projected Revenue, and Financial Projections

Projected Revenue Related to the Proposed Program

1st Ye ar 2nd Ye ar 3rd Ye ar 4th Ye ar 5th Ye ar † † † † Revenues Academic Year Academic Year Academic Year Academic Year Academic Year Tuition Revenue 01. From Existing Sources $458,280 $519,060 $561,724 $8,649 $8,822 02. From New Sources $247,230 $272,190 $297,004 $329,287 $335,872 03. Total $705,510 $791,250 $858,728 $337,936 $344,694 Other Revenue 07. From Existing Sources§ $0 $0 $0 $0 $0 08. From New Sources** $0 $0 $0 $0 $0 09. Total $0 $0 $0 $0 $0 Grand Total[8] 10. From Existing Sources§ $458,280 $519,060 $561,724 $8,649 $8,822 11. From New Sources** $705,510 $791,250 $858,728 $337,936 $344,694 TOTAL $705,510 $791,250 $858,728 $337,936 $344,694

Year 1 Year 2 Year 3 Year 4 Year 5 PART-TIME STAFF (do not include library staff in this section) Part Time Staff Base Salary (list separately) Faculty Replacement Costs (replacement of full-time faculty - e.g. on release time - with part-time faculty) Graduate Assistants Student Hourly Part Time Employee Fringe Benefits (24.3%) 0 0 0 0 0 Total (Links to Part-Time Staff on Program Exp Worksheet) $ - $ - $ - $ - $ -

LIBRARY Library Resources Library Staff Full Time (List Separately) Full Time Staff Fringe Benefits (41.6%) 0 0 0 0 0 Library Staff Part Time (List Separately) Part Time Employee Fringe Benefits (24.3%) 0 0 0 0 0 TOTAL (Links to Library on Program Exp Worksheet) $ - $ - $ - $ - $ -

EQUIPMENT Computer Hardware 10000 Office Furniture Other (Specify) Total (Links to Equipment on Program Exp Worksheet) $ 10,000.00 $ - $ - $ - $ -

LABORATORIES Laboratory Equipment Other (list separately) TOTAL (Links to Laboratories on Program Exp Worksheet) $ - $ - $ - $ - $ -

Year 1 Year 2 Year 3 Year 4 Year 5 SUPPLIES AND EXPENSES (OTPS) Consultants and Honoraria Office Supplies Instructional Supplies Faculty Development Travel and Conferences Membership Fees Advertising and Promotion Accreditation Computer Software Computer License Fees Computer Repair and Maintenance Equipment Repair and Maintenance New Total Supplies and OTPS Expenses (Links to Supplies on Program Exp Worksheet) $ - $ - $ - $ - $ -

CAPITAL EXPENDITURES Facility Renovations Classroom Equipment Other (list separately) TOTAL (Links to Capital Expenditures on Program Exp Worksheet) $ - $ - $ - $ - $ -

Other (list separately)

TOTAL (Links to Other on Program Exp Worksheet) $ - $ - $ - $ - $ -

The Five-Year Revenue Projections for Program SENIOR COLLEGE (UNDERGRADUATE) WORKSHEET Year 1 = Fall 2020

EXISTING FULL-TIME STUDENTS Year One Year Two Year Three Year Four Year Five Tuition & Fees: # of EXISTING FULL-TIME, In-State Students (linked from "Enroll & Seat Need Projections") 76 82 87 93 93

Tuition Income (calculates 2% increase per year after Fall 2015) $6,030 $6,330 $6,457 $93 $95 Total Tuition $458,280 $519,060 $561,724 $8,649 $8,822 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total In-State Tuition & Fees $458,280 $519,060 $561,724 $8,649 $8,822

Tuition & Fees: # of EXISTING FULL-TIME, Out-of-State Students (linked from "Enroll & Seat Need Projections") 0 0 0 0 0 Annual Avg # of Credits per FT student (24-30) Tuition Income (Specify Rate per credit. Calculates 2% annual increase after Fall 2015) $535 $560 $571 $583 $594 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total Out-of-State Tuition & Fees $0 $0 $0 $0 $0

TOTAL EXISTING FULL-TIME TUITION REVENUE $458,280 $519,060 $561,724 $8,649 $8,822

EXISTING PART-TIME STUDENTS Year One Year Two Year Three Year Four Year Five Tuition & Fees: # of EXISTING PART-TIME, In-State Students (linked from "Enroll & Seat Need Projections") 0 0 0 0 0 Total Enrolled Credits (Enter Avg # credits per student per year- Fall+ Spring+Summer -- i.e. 6 Fall, 6 Spring, 3 Summer=15) Tuition Income (Specify Rate per credit. Calculates 2% increase per year after Fall 2015) $260 $275 $281 $286 $292 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total In-State Tuition & Fees $0 $0 $0 $0 $0

Tuition & Fees: # of EXISTING PART-TIME Out of State Students (linked from "Enrollment and Seat Need Projections") 0 0 0 0 0 Total Enrolled Credits (Enter Avg # credits per student per year- Fall+ Spring+Summer -- i.e. 6 Fall, 6 Spring, 3 Summer=15) Tuition Income (Specify Rate per credit. Calculates 2% increase per year after Fall 2015) $535 $560 $571 $583 $594 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total Out-of-State Tuition & Fees $0 $0 $0 $0 $0

TOTAL EXISTING PART TIME REVENUE $0 $0 $0 $0 $0 TOTAL EXISTING REVENUE (LINKS TO REVENUE SPREADSHEET ROW 5) $458,280 $519,060 $561,724 $8,649 $8,822

NEW FULL-TIME STUDENTS Year One Year Two Year Three Year Four Year Five Tuition & Fees: # of NEW FULL-TIME, In-State Students (linked from "Enroll & Seat Need Projections") 41 43 46 50 50

Tuition Income (Calculates 2% increase per year after Fall 2015) $6,030 $6,330 $6,457 $6,586 $6,717 Total Tuition $247,230 $272,190 $297,004 $329,287 $335,872 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total In-State Tuition & Fees $247,230 $272,190 $297,004 $329,287 $335,872

Tuition & Fees: # of NEW FULL-TIME, Out-of -State Students (linked from "Enroll & Seat Need Projections") 0 0 0 0 0 Annual Avg # of Credits per FT student (24-30) Tuition Income (Specify Rate per credit. Calculates 2% increase per year after Fall 2015) $535 $560 $571 $583 $594 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total Out-of-State Tuition & Fees $0 $0 $0 $0 $0

TOTAL NEW FULL-TIME TUITION REVENUE $247,230 $272,190 $297,004 $329,287 $335,872

NEW PART-TIME STUDENTS Year One Year Two Year Three Year Four Year Five Tuition & Fees: # of NEW PART-TIME, In-State Students (linked from "Enroll & Seat Need Projections") 0 0 0 0 0 Total Enrolled Credits (Enter Avg # credits per student per year- Fall+ Spring+Summer -- i.e. 6 Fall, 6 Spring, 3 Summer=15) Tuition Income (Specify Rate per credit. Calculates 2% increase per year after Fall 2015) $260 $275 $281 $286 $292 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total In-State Tuition & Fees $0 $0 $0 $0 $0

Tuition & Fees: # of NEW PART-TIME, Out-of-State Students 0 0 0 0 0 Total Enrolled Credits (Enter Avg # credits per student per year- Fall+ Spring+Summer -- i.e. 6 Fall, 6 Spring, 3 Summer=15) Tuition Income (Specify Rate per credit) calculates 2% increase per year $535 $560 $571 $583 $594 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total Out-of-State Tuition & Fees $0 $0 $0 $0 $0

TOTAL NEW PART-TIME REVENUE $0 $0 $0 $0 $0 TOTAL NEW REVENUE (LINKS TO REVENUE SPREADSHEET ROW 7) $247,230 $272,190 $297,004 $329,287 $335,872

OTHER REVENUE Year One Year Two Year Three Year Four Year Five Other Revenue From Existing Sources (specify and explain)-LINKS TO REVENUE SPREADSHEET ROW 13) Other Revenue New (specify and explain) (LINKS TO REVENUE SPREADSHEET ROW 15)

I.B.2 –LaGUARDIA COMMUNITY COLLEGE – AS in PHYSICAL SCIENCES

WHEREAS, the need to increase the number of science, technology, engineering and mathematics (STEM) majors has been an important focus in the United States in recent years, and

WHEREAS, job growth for the chemistry and material science fields are projected to increase 6% and the projected growth rate in physics is 10%, and

WHEREAS, LaGuardia Community College has strong faculty in the sciences, and the College has a robust curriculum in chemistry, physics and mathematics, and

WHEREAS, not all students are prepared to begin their studies at senior colleges and community colleges are a nurturing environment for students needing extra support, and

WHEREAS, having a separately registered program will enhance student recruitment, advisement and tracking, allowing for a better student experience, and

WHEREAS, LaGuardia has established two articulation agreements with York College so that successful students may seamlessly transfer into York’s well-established bachelor’s degrees in Chemistry and Physics, and

WHEREAS, an A.S. in Physical Sciences will allow for the smooth transfer of LaGuardia graduates to Chemistry and Physics majors at York College, and

WHEREAS, this program will help increase the number of underrepresented students in STEM disciplines and professions, so be it

RESOLVED, that the program in Physical Sciences at LaGuardia Community College, leading to the Associate of Science, be approved effective May 8, 2019, subject to financial ability.

EXPLANATION: In consultation with colleagues at York College, LaGuardia has packaged its existing courses in chemistry, physics and mathematics currently offered within its multidisciplinary Liberal Arts and Science major in order to create a curriculum that prepares students for transfer to a number of baccalaureate programs. The A.S. in Physical Sciences will enable students at LaGuardia to take a rigorous course load in physical sciences that is common to both physics and chemistry programs in colleges and universities throughout the country, and which are matched with the first two years of study at York College in chemistry and physics. In addition to the two existing articulation agreements with York, agreements with other senior colleges are also in preparation.

LAGUARDIA COMMUNITY COLLEGE AND YORK COLLEGE

THE CITY UNIVERSITY OF NEW YORK

PROPOSAL TO ESTABLISH A PROGRAM IN ASSOCIATE OF SCIENCE (A.S.) PROGRAM IN PHYSICAL SCIENCES SPONSORED BY THE NATURAL SCIENCES DEPARTMENT OF LAGUARDIA COMMUNITY COLLEGE AND SCHOOL OF ARTS AND SCIENCES OF YORK COLLEGE

College Governance Approval

LaGuardia Community College Curriculum Committee, LaGuardia Community College Senate,

College Representative: Dionne A Miller, Interim Associate Dean for Academic Affairs Contact:

Telephone: 718-482-5741 Email: [email protected]

Provost’s Signature:

Provost’s Name: Paul Arcario Provost and Senior Vice President for Academic Affairs LaGuardia Community College

TABLE OF CONTENTS

Contents Page Executive Summary ………………………………………………………... 3 I. Abstract …………………………………………………………………….. 4 II. Purpose and Goals ………………………………………………………….. 4 III. Need and Justification ……………………………………………………… 5 IV. Students …………………………………………………………………….. 6 A. Interest/Demand …………………………………………………… 6 B. Enrollment Projections (Table Included) ………………………….. 8 C. Admission Requirements ………………………………………….. 8 V. Curriculum …………………………………………………………………. 8 VI. Curriculum Requirements (LaGuardia Community College) ……………… 10 VII. Curriculum Requirements (York College) ………………………………… 13 VIII. Cost Assessments ………………………………………………………….. 15 A. Faculty ……………………………………………………………... 15 B. Facilities and Equipment …………………………………………... 15 C. Library and Instructional Materials ………………………………... 15 D. Budget Tables ……………………………………………………… 15 IX. Evaluation ………………………………………………………………….. 15 X. Appendices ………………………………………………………………… 17

APPENDIX A COURSE DESCRIPTIONS FOR REQUIRED COURSES APPENDIX B FACULTY TEACHING ASSIGNMENTS (SED Form) APPENDIX C FINANCIAL TABLES (CUNY) APPENDIX D ARTICULATION AGREEMENT(S) (CUNY) APPENDIX E GENERAL INFORMATION (SED Form)

EXECUTIVE SUMMARY

The proposed A.S. degree program in Physical Sciences was initiated at LaGuardia Community College (LaGCC) out of a partnership with York College (YC) to enable LaGCC students interested in Chemistry and Physics to articulate to YC in these majors. The A.S. in Physical Sciences will enable students at LaGCC to take a rigorous course load in physical sciences that is common to both physics and chemistry programs in colleges and universities throughout the country, and which are matched with the first two years of study at YC in chemistry and physics. Students will have the option of declaring either the Chemistry or Physics track. The main objectives of developing this program are to make LaGCC a desirable place for students to study the physical sciences with the ability to start as a junior at YC, and to contribute to the growth of the Natural Sciences programs at LaGCC and the Chemistry and Physics baccalaureate programs at York College.

The need to increase the number of science, technology, engineering and mathematics (STEM) majors has been an important focus of the nation in recent years. Job growth for chemistry and material science fields are projected to grow 6%, while in physics it is 10%. Additionally, a US News and World Report (Feb 24, 2015) article reports that despite a national focus on directing more students toward STEM fields – particularly women and minorities – the STEM workforce is no more diverse now than in 2001. There is therefore still a need to encourage minority students and women to enter STEM careers.

In an effort to meet the New York City and State’s workforce demands, several CUNY community colleges are in the process of establishing Associate Degree programs that will lead students directly into baccalaureate programs. This program proposal is for an A.S. in Physical Sciences that will allow the smooth transfer of LaGCC graduates to the Chemistry and Physics majors at York College.

Students who have expressed an interest in majoring in a field of physical science at LaGCC currently only have the Liberal Arts Math and Science track available to them. This track often does not enable students to start as upperclassmen when they go to their senior college: this program will address this issue. The diversity of the LaGCC student body will also encourage more minority students in the Physical Sciences program, who are vastly underrepresented in the physical sciences fields, to enter such programs, and will serve the purpose of assisting those students to achieve their goal in partnership with York College.

A survey was conducted in Spring 2016 at LaGCC among the Liberal Arts: Mathematics and Science students as well as other students declaring as science majors, to measure their interest in the proposed Physical Sciences Program. The survey responses demonstrated substantial student interest in the possibility of pursuing and AS in Physical Sciences at LaGCC, with over 70% of respondents indicating some interest in these. The survey was distributed to LaGCC students enrolled in the Natural Sciences and Liberal Arts first year seminars.

Queens to introduce the Physical Sciences program to high school seniors. The program will be publicized through the Office of Admissions and through direct recruiting by members of the Natural Sciences Department. We also intend to work closely with our articulation partners to identify students with a strong interest in Physical Sciences who might be in need of the more supportive academic program of a community college to improve their chances of success. Students will also be recruited from LaGuardia’s Liberal Arts: Math and Science major.

I ABSTRACT

The mission of LaGuardia Community College of the City of University of New York is to educate and graduate one of the most diverse student populations in the country to become critical thinkers and socially responsible citizens who help to shape a rapidly evolving society. The projected Associates of Science degree program in Physical Sciences at LaGuardia Community College (LaGCC) as articulated with York College (YC) will enable LaGCC students interested in Chemistry and Physics to articulate to YC in these majors. The A.S. in Physical Sciences will enable students at LaGCC to take a rigorous course load in physical sciences that is common to both physics and chemistry programs in colleges and universities throughout the country, and which are matched with the first two years of study at YC in chemistry and physics. The main objectives of developing this program are to make LaGCC a desirable place for students to study the physical sciences leading to a B.S. degree program, and to contribute to the growth of the Natural Sciences programs at LaGCC and the Chemistry and Physics baccalaureate programs at York College.

II PURPOSE AND GOALS

The Department of Natural Sciences at LaGuardia Community College (LaGCC) initiated an articulation agreement with York College (YC) in Fall 2017. Students who have expressed an interest in majoring in a physical science field at LaGCC currently only have the Liberal Arts: Math and Science track available to them. This track often does not enable students to start as upperclassmen when they go to their 4-year college; this program will address this issue. This program will also encourage more minority students, who are vastly underrepresented in the physical sciences, to enter programs in physical science fields and will serve the purpose of assisting those students to achieve their goal in partnership with York College. This program will contribute to LaGCC’s strategic planning goal of raising academic quality and improving student success and will contribute to the growth and expansion of the Natural Sciences Department at LaGCC. The Program will also serve to accomplish the College’s long-term mission by offering students career training and preparing them to participate in and take advantage of the many scientific institutions in New York City. The joint program between LaGCC and YC will offer students an additional educational pathway leading to an exciting career in Physical Sciences and also provide students with skills to think critically, to read and communicate (verbally and written), all which are essential skills for a career in science, technology, engineering and mathematics (STEM) fields.

The physics and chemistry programs at YC have been highly successful and we believe that LaGCC students would be very interested and benefit from this field of study. LaGCC has received a letter of support from YC for LAGCC’s development of an A.S. degree in the

Physical Sciences, leading to the B.S. in either Chemistry or Physics from York College. Students completing all degree requirements and graduating from LaGCC will be able to transfer to the Physics or Chemistry Program at York College to earn a Bachelor of Science Degree, depending on the track they completed at LaGCC. Strong student interest in science at LaGuardia has facilitated the development of a new Biology major and an Environmental Science major. The proposed Physical Sciences program will complement the existing science programs and appeal to students who wish to study Chemistry or Physics. Students moving on to study Chemistry or Physics at York have a broad range of options available to them including majors in Chemistry, Physics and Pharmaceutical Sciences and/or minors in Biochemistry or Astronomy.

III NEED AND JUSTIFICATION

The need to increase the number of STEM majors has been an important focus of the nation in recent years. STEM fields are considered increasingly central to U.S. economic competitiveness and growth. A 2007 Department of Labor report stressed the need for “coordinated efforts among public, private, and not-for-profit entities to promote innovation and to prepare an adequate supply of qualified workers for employment in STEM fields.” This sentiment was echoed in a report by the National Science Foundation in 2015 on the STEM workforce; additionally, this report asserted that “STEM knowledge and skills enable multiple, dynamic pathways to STEM and non-STEM occupations alike.” In 2014, the Bureau of Labor and Statistics reported that STEM jobs now make up more than 1 out of every 10 jobs in the United States and have wages that are approaching nearly twice the U.S. average. According to the Bureau, in May 2016, there were 8.8 million science, technology, engineering, and mathematics (STEM) jobs, representing 6.3 percent of U.S. employment. Additionally, employment in occupations related to STEM is projected to grow to more than 9 million between 2012 and 2022, an increase of about 1 million jobs over 2012 employment levels (Occupational Outlook Quarterly, Spring 2014). Specifically, job growth for chemistry and material science fields are projected to grow 3%, while in physics it is 7%. (Occupational Outlook Quarterly, Fall 2014). Additionally, a US News and World Report (Feb 24, 2015) article reports that despite a national focus on directing more students toward STEM fields – particularly women and minorities – the STEM workforce is no more diverse now than in 2001. There is therefore still a need to encourage minority students and women to enter STEM careers.

The proposed Physical Sciences major at LaGuardia Community College will be uniquely situated to address these issues. First, students graduating from the proposed A.S. degree program in Physical Sciences at LaGCC will have a strong foundation in the physical sciences and will be prepared to enter baccalaureate programs in chemistry, physics, material science, nanotechnology/nanoscience, pharmaceutical sciences as well as pre-medical programs. Second, LaGuardia has a diverse population of students from almost 154 countries. The LaGuardia Community College Institutional Research Profile 2017 showed that 58% of credit students were female and 67% were African-American or Hispanic. The diverse nature of the LaGuardia student population therefore provides an excellent source of potential STEM professionals to increase ethnic and gender diversity in STEM fields.

IV STUDENTS

A) Interest & Demand

A survey was conducted in spring 2016 at LaGCC among the Liberal Arts: Mathematics and Science students as well as other students declaring as science majors, to measure their interest in the proposed Physical Sciences Program. The survey responses demonstrated student interest in the possibility of pursuing an A.S. in Physical Sciences at LaGCC. The survey was distributed to all LaGCC students enrolled in the Natural Sciences and Liberal Arts first year seminars.

The following is the summary from the Spring I 2016 survey:

The survey generated 41 responses. Out of the students who responded to the survey, 70.7% demonstrated an interest in a Physical Sciences program. Of those students showing an interest in Physical Sciences, nearly all of them indicated they would be interested in completing the Associates Degree at LaGuardia; 15% indicated they would be very interested or interested in transferring to YC. The majority of respondents wanted to further their studies after completing their Associates Degree, with 23.1% wanting to complete a Bachelor’s Degree, 25.6% a Master’s Degree and 48.7% a Doctoral Degree.

The new program seeks to recruit students from LaGCC, especially females and minority students. Traditionally, minorities have been a very small portion of the high paying jobs in STEM fields. LaGCC has a number of successful programs that encourage minority students to enter the STEM fields. These programs include the New York City Louis Stokes Alliance for Minority Participation in Science, Mathematics, Engineering and Technology (NYC-LSAMP), the CUNY Research Scholars Program (CRSP) and the National Institute of Health’s Bridges to the Future research program. The diverse nature of the LaGuardia student population provides an excellent source of potential minority professionals. According to LaGCC’s Office of Institutional Research and Assessment 2017 report, 49% of the LaGuardia student population was Hispanic, 18% was Black, and 58% were female.

Science courses have a reputation for being both challenging and time consuming. In order for students to be successful in a science program they must be able to complete the science courses at the college and courses themselves must be rigorous enough to get these students ready for upper division course work. The data in the table below indicates the number of each type of grade received in advanced physical science courses between Fall 2012 and the Spring of 2016: the table includes data on both authorized and unauthorized withdrawals. A course or sequence of courses is designated as an advanced course if it has a two course college credit bearing sequence prerequisite. All of the courses in the table below require either the introductory general chemistry sequence (SCC 201 & SCC 202) or the yearlong sequence of precalculus and calculus 1 (MAT 200 & MAT 201). This table represents a total of over 5200 student attempts in these advanced courses and analysis of the data shows that 66% of these attempts resulted in a grade of a C or higher, which is required for a course in the major to transfer to a four-year college. Additionally, nearly 75% of these attempts resulted in a passing grade, a grade of D- or higher. In considering this data along in conjunction with a nearly 50% pass rate of course in the introductory science sequence i.e. General Chemistry I & II we have shown that the students at LaGCC are capable of succeeding in these difficult classes and that the courses themselves provide the necessary rigor to prepare students to succeed at a four-year college.

Result of Student Attempts in Advanced Science Courses at LaGuardia Community College (Fall 2012 – Spring 2016):

DEPT Course code A A- B B- B+ C C+ Total MAT 202 313 104 124 68 98 132 65 904 MAT 203 212 66 94 42 34 76 59 583 MAT 204 135 50 62 28 36 56 22 389 MAT 212 128 17 46 13 22 28 21 275 SCC 251 37 28 24 22 17 18 15 161 SCC 252 30 23 12 9 14 16 5 109 SCP 231 173 73 81 80 95 59 55 616 SCP 232 118 44 67 41 59 33 40 402

DEPT Course code C- D D- D+ F INC W WD WN WU Total MAT 202 58 59 21 24 311 16 313 28 11 45 828 MAT 203 29 33 17 18 126 4 59 1 7 9 274 MAT 204 9 30 7 5 64 0 59 5 4 6 180 MAT 212 4 7 0 11 27 3 11 0 2 2 63 SCC 251 8 11 4 7 20 1 40 2 0 4 89 SCC 252 9 2 2 2 9 0 9 0 1 0 25 SCP 231 31 18 4 8 89 3 114 12 4 6 258 SCP 232 11 5 3 3 26 3 26 0 2 0 68

The Natural Sciences Department will work with the College’s Admissions and Marketing Offices to develop a recruitment plan for this major. It is also our intention to utilize the strong connections the College has established with the public and private high schools of western Queens to introduce the Physical Sciences program to high school seniors. The program will be publicized through the Office of Admissions and through direct recruiting by members of the Natural Sciences Department. We also intend to work closely with our articulation partners to identify students with a strong interest in Physical Sciences who might be in need of the more supportive academic program of a Community College to improve their chances of success. Students will also be recruited from LaGuardia’s Liberal Arts: Math and Science major.

B) Enrollment Projections

The proposed Associates of Science for Physical Sciences program will build from the Liberal Arts: Math and Science A.S. degree. Over the past six years, the Natural Sciences Department has developed two new programs, Biology and Environmental Science, and is currently developing a Forensic Sciences major. The proposed Physical Sciences program will offer students an alternative science major to explore and develop career opportunities. The projected enrollment is outlined in the table below.

Projected Headcount 1st year 2nd year 3rd year 4th year 5th year (Percentage new) Full-time 20 30 40 50 50 Part-time 5 5 5 10 10 Total 25 35 45 60 60

C) Admission Requirements

Admission requirements for students who enroll in the A.S. in Physical Sciences Program will be the same for all applicants to majors within the Natural Sciences Department. Prior to admission to LaGCC, students are afforded an opportunity to speak with an admissions counselor before submitting their application to CUNY. During LaGCC’s Information sessions, counselors will review the areas of study, the college’s academic calendar, student services, and the internship program offered at LaGCC. Counselors will also guide students through the application process step-by-step, highlighting documentation requirements, deadlines and how to apply online. Information sessions are also available in Spanish. After students receive a letter of admission, the Testing Office will arrange a time to take the ACT placement exams. This does not apply to students who are exempt. Students who apply to the Natural Sciences Department will be required to take the First Year Seminar for Natural Sciences, in addition to seeking advisement from the Student Advisement Physical Sciences team.

V CURRICULUM

In order to graduate with an Associate in Science degree from LaGCC, students must complete the First Year Seminar in Natural Sciences, NSF101 as well as 14 credits in the required core and 20 credits in the flexible core (see table below). Students transferring to YC in Chemistry or Physics must also have completed the appropriate program core outlined in the table below. Both the chemistry track and the physics track require one year of chemistry, one year of calculus and one year of calculus based physics.

General Education Courses

The General Education courses selected for the curriculum follow the LaGuardia distribution guidelines for an Associate in Science degree program, and articulate with General Education course requirements at York College via Pathways.

Major courses for the BS at York In addition to the required 60 credit program at LaGuardia, students will need to take courses required by the chemistry or physics B.S. Programs as outlined to bring their total number of credits to 120 and satisfy the residency requirements of York College.

Course requirements to fulfill the articulation agreements in chemistry and physics:

An outline of curricular requirements for the proposed A.S. in Physical Sciences follows.

Students enrolled in the Physical Sciences program at LaGCC must:  Have a GPA of 2.5 in the science major courses at the time of admission to YC.  Have a minimum C grade in all mathematics and science courses  Satisfy foundation science courses at LaGCC before entering the junior year of the Physics or Chemistry major at YC. Listed below are these courses: • One year of college-level, major’s Chemistry • One year of college-level, major’s Organic Chemistry (Chemistry Track only) • One year of college-level, major’s Physics w/ Calculus (Physics Track only) • One year of college-level, major’s Calculus

Student support and advisement

Students in the Physical Sciences major would benefit from outstanding advisement and support in completing their program. LAGCC’s advisement model seeks to provide meaningful advising contacts for all students at all stages of their academic journey. Divided into tiers based on GPA, every Physical Sciences major would have at least one advising contact per semester with a peer advisor, a faculty member or a college advisor depending on their tier placement. The methods include one-on-one advising, group advising, advising events, and virtual advisement interactions. The “Advising by Major” model capitalizes on the most salient point of connection and identification for most students, namely, their major. Students are connected to faculty in the Physical Sciences major early on, through the locus for first-semester advisement, the Natural Sciences First Year Seminar taught by Natural Sciences faculty. Student Affairs also provides additional support to the advisement process from Admissions, Career and Employment Services, Transfer Services and Financial Aid. By drawing upon both faculty and advising staff and Student Affairs staff as appropriate, LaGCC aims to provide advisement continuity for students in the major throughout their academic career at the college, with the intention of making advisement an integral, personalized, ongoing, and “intrusive” part of students’ experience. The aspirational goal for the advisement model is to guide all students successfully to graduation.

Additionally, LaGCC is building a transfer partnership with YC through its grant-funded STEM- CONNECT program, to go beyond institutional articulation to build relationships between people, processes, and systems. To begin, the articulation agreement developed with YC for the Physical Sciences major ensures that LaGCC course credits transfer effectively, and will reduce the number of steps required of students. LaGCC Physical Sciences graduates will be granted junior status and equal access to key courses and programs. Both colleges have committed to data sharing, and to engaging in evidence-based partnership activities designed to support

transfer success, including: annual cross campus transfer summits; regular faculty-faculty research exchanges; cross-campus advisement events; and designated support services for post transfer students, such as transfer orientation and transfer mentors. Physical Sciences majors from LaGCC will thus have a clear path laid out for them upon degree completion to the transfer college, which will connect them to people and services at YC.

Lastly, LaGCC provides outstanding tutoring support for students in science and mathematics courses. The Natural Sciences department provides peer tutors through the Science Study Center in chemistry and physics; the Academic Peer Instruction (API) Program provides trained peer tutors assigned to specific high-risk courses in chemistry, physics and mathematics; the Student Government Association provides peer tutors through its Study Hall and the Math Tutoring Lab provides mathematics tutoring and exam review programs. Students also benefit from LaGCC’s Writing Center, which helps students with any part of the writing process.

VI Curriculum Requirements (LaGuardia Community College) A. S. Physical Sciences: Chemistry and Physics Tracks Course Requirements

Chemistry Track Physics Track

PATHWAYS COMMON CORE: 30 credits PATHWAYS COMMON CORE: 30 credits

A. Required Core: 12 credits A. Required Core: 12 credits

English: 6 credits English: 6 credits Mathematical and Quantitative Reasoning: 3 Mathematical and Quantitative Reasoning: 3 credits credits Life & Physical Sciences: 3 credits Life & Physical Sciences: 3 credits

B. Flexible Core: 18 credits B. Flexible Core: 18 credits Select one course from each of the five flexible Select one course from each of the five flexible core categories and one additional course from core categories and one additional course from any flexible core category. Note: Students can any flexible core category. Note: Students can select only two courses from any one discipline. select only two courses from any one discipline.

World Cultures and Global Issues World Cultures and Global Issues US Experience in its Diversity US Experience in its Diversity Creative Expression Creative Expression Individual and Society Individual and Society Scientific World Scientific World

Students are required to select one Urban Study Students are required to select one Urban Study course to complete college requirement. course to complete college requirement.

MAJOR REQUIREMENTS: 30 credits MAJOR REQUIREMENTS: 30 credits

NSF 101 First Year Seminar in Natural Sciences NSF 101 First Year Seminar in Natural Sciences 2 2

*MAT115 (or MAT117 depending on placement *MAT115 (or MAT117 depending on scores) 3 (MQR) placement scores) 3 (MQR) *MAT200 Precalculus 4 (SW) *MAT200 Precalculus 4 (SW) MAT201 Calculus I 4 MAT201 Calculus I 4 MAT202 Calculus II 4 MAT202 Calculus II 4 MAT203 Calculus III 4 *SCC201 General Chemistry I (LPS) *SCC202 General Chemistry II (SW) *SCP231 General Physics I 4 (LPS) SCC251 Organic Chemistry I 5 SCP232 General Physics II 4 SCC252 Organic Chemistry II 5 SCP233 Modern Physics 4

SCP232 General Physics I 4 SCC201 General Chemistry I

SCP 211 Research Methods in Physical Sciences SCP 211 Research Methods in Physical 3 Sciences 3

Unrestricted elective: 2 credits Total: 60 credits Total: 60 credits *This program has a waiver to require STEM courses in the Common Core. Students are *This program has a waiver to require STEM advised to select these courses in the Pathways courses in the Common Core. Students are Common Core area indicated in brackets, in advised to select these courses in the Pathways order to complete the degree within 60 credits. Common Core area indicated in brackets, in If students take other courses in these areas, order to complete the degree within 60 credits. they will be certified as having completed the If students take other courses in these areas, Common Core area, but it may not be possible they will be certified as having completed the for them to finish their degree programs Common Core area, but it may not be possible within the regular number of credits. for them to finish their degree programs within the regular number of credits.

Freshman and sophomore course sequences at LaGuardia Community College: Chemistry Track (60 credits)

FRESHMAN YEAR: FALL FRESHMAN YEAR: SPRING Course # Course Title Crs. Course # Course Title Crs. MAT 115 (or College Algebra and 3 SCC 202 General Chemistry 4 MAT117) Trigonometry II Flexible Core World Cultures and 3 ENG 102 Writing through 3 Global Issues Literature NSF 101 New to College 2 Flexible Core Creative 3 Seminar Expression ENG 101 Composition I 3 MAT 200 Precalculus 4 SCC 201 General Chemistry I 4 SEMESTER TOTAL 15 SEMESTER TOTAL 14 TOTAL PROGRAM CREDITS 15 TOTAL PROGRAM CREDITS 29

SOPHOMORE YEAR: FALL SOPHOMORE YEAR: SPRING Course # Course Title Crs. Course # Course Title Crs. SCP 231 General Physics I 4 SCC 252 Organic Chemistry II 5 MAT 201 Calculus I 4 MAT 202 Calculus II 4 Flexible Core Individual and Society 3 SCP 211 Research Methods in 3 Physical Sciences (Capstone) SCC 251 Organic Chemistry I 5 Flexible Core US Experience in its 3 Diversity SEMESTER TOTAL 16 SEMESTER TOTAL 15 TOTAL PROGRAM CREDITS 45 TOTAL PROGRAM CREDITS 60

ALL COURSES IN THIS CURRICULUM ARE LIBERAL ARTS AND SCIENCE COURSES

Freshman and Sophomore Course Sequences at LaGuardia Community College: Physics Track (60 credits)

FRESHMAN YEAR: FALL FRESHMAN YEAR: SPRING Course # Course Title Crs. Course # Course Title Crs. MAT 115 (or College Algebra and 3 SCP 231 General Physics I 4 MAT117) Trigonometry Flexible Core World Cultures and 3 ENG 102 Writing through 3 Global Issues Literature NSF 101 New to College 2 SCC201 General Chemistry I 4 Seminar ENG 101 Composition I 3 MAT201 Calculus I 4 MAT 200 Precalculus 4 SEMESTER TOTAL 15 SEMESTER TOTAL 15 TOTAL PROGRAM CREDITS 15 TOTAL PROGRAM CREDITS 30

SOPHOMORE YEAR: FALL SOPHOMORE YEAR: SPRING Course # Course Title Crs. Course # Course Title Crs. SCP 232 General Physics II 4 SCP 233 Modern Physics 4 MAT 202 Calculus II 4 MAT 203 Calculus III 4 Flexible Core Individual and 3 SCP 211 Research Methods in 3 Society Physical Sciences (Capstone) Flexible Core US Experience in its 3 Unrestricted Unrestricted 2 Diversity Elective elective Flexible Core Creative Expression 3 SEMESTER TOTAL 14 SEMESTER TOTAL 16 TOTAL PROGRAM CREDITS 44 TOTAL PROGRAM CREDITS 60

ALL COURSES IN THIS CURRICULUM ARE LIBERAL ARTS AND SCIENCES COURSES

VII Curriculum Requirements B. S. Degree at York College

Junior and Senior Year Course Sequences at York College in Chemistry (60 credits)

JUNIOR YEAR: FALL JUNIOR YEAR: SPRING Course # Course Tittle Crs. Course # Course Tittle Crs. CHEM 310 Inorganic Chemistry 3 CHEM 322 Physical Chemistry- 3 Quantum Chemistry CHEM 321 Physical Chemistry- 3 CHEM 342 Instrumental Analysis II 3 Thermodynamics CHEM 341 Instrumental Analysis I 3 CHEM 330 Structure and Mechanism 3 in Biochemistry MATH 122 Analytic Geometry and 4 Foreign Language 3 Calculus III Foreign Language 3 Free electives 3 SUBTOTAL 16 SUBTOTAL 15 TOTAL 76 TOTAL 91

SENIOR YEAR: FALL SENIOR YEAR: SPRING Course # Course Tittle Crs. Course # Course Tittle Crs. CHEM 339 OR Heterocyclic Chemistry 3 CHEM 462 OR Experiments in Biological 3 and Drug Chemistry Chemistry OR CHEM 450 OR OR CHEM 490 CHEM 460 Advanced Topics in Independent Study Chemistry OR Biochemistry I WRT 302 Research & Writing for 3 CHEM 421 Physical-Inorganic 3 Sciences, Mathematics Laboratory & Technology Program electives 6 PHYS 118 University Physics II 4 Unrestricted Elective 3 PHYS 114 Physics Lab II 1 Unrestricted Elective 3 SUBTOTAL 15 SUBTOTAL 14 TOTAL 106 TOTAL 120

Junior and Senior year course sequences at York College in Physics (60 credits)

JUNIOR YEAR: FALL JUNIOR YEAR: SPRING Course # Course Tittle Crs. Course # Course Tittle Crs. Foreign 4 Foreign Language 4 Language Cultural 3 Fine & Perf. Arts 3 Diversity 200 Level Speech 101 Oral Comm. in 3 PHYS 312 Class. Mechanics I 3 Contemp. Society PHYS 231 Oscillations & Waves 3 PHYS 321 Electricity and Magnetism 3 I PHYS 383 Modern Phys. Lab 3 MATH 223 Differential Equations and 3 Dynamical Systems SUBTOTAL 16 SUBTOTAL 16 TOTAL 76 TOTAL 92

SENIOR YEAR: FALL SENIOR YEAR: SPRING Course # Course Tittle Crs. Course # Course Tittle Crs. MATH 333 Linear Algebra 4 PHYS 441 Quantum Mechanics & 3 Atomic Physics Physics/Astronomy 3 Physics/Astronomy 3 Elective Elective Physical Education 2 CHEM 111 Principles of 3.5 150 Chemistry II PHYS 351 Stat. Mechanics 3 CHEM 112 Principles of 1.5 Chemistry II: Laboratory Unrestricted Elective 3 Unrestricted Elective 2 SUBTOTAL 15 SUBTOTAL 13 TOTAL 107 TOTAL 120

VIII- Cost Assessments

A) F aculty

See Appendix B.

B) Facilities and Equipment

Existing facilities and equipment will be enough to meet program needs. LaGuardia laboratories have been upgraded: a Carbon-Hydrogen-Nitrogen analyzer, two Ultraviolet visible (UV-VIS) spectrometers, and an infrared spectrometer were acquired. The equipment in these labs will also be used for the Physical Sciences program. It is expected that this augmentation of lab facilities will be sufficient to handle the increase in enrollment projected for the Physical Sciences program. There will be no need to locate additional space in the lab and/or alternate areas to store the supplies, materials and instructional materials for this program. The computer labs will be used to assist students in the development of their student e-Portfolios.

C) Library and Instructional Materials

In 2014, the LaGuardia Community College library began a major renovation project. The new state of the art library will be designed “to promote learning and student engagement and the technology upgrades will enhance the student experience.” The renovated library is expected to grow by 21,000 square feet and will feature 312 new seats powered with Internet connections at each, a reading room, media lab, group study rooms and additional office and support space. The students enrolled in the Physical Sciences Program will be able to utilize the upgraded library for the development of research assignments and projects.

D) Budget Tables

See Appendix C

IX - Evaluation

At LaGCC the progress of the program will be monitored by the Natural Sciences Department and the Office of Academic Affairs, with emphasis on enrollment patterns, retention and graduation rates. In addition, LaGCC and YC are committed to the success of the program and will institute periodic collaborative assessments of the program to be coordinated through the Office of Academic Affairs at LaGCC and YC and involving the Offices of Institutional Research at both campuses. Information received from either College may result in the modification and revision of courses or curriculum (see Appendix).

LaGCC’s outcomes assessment plan is designed to assess institutional effectiveness in terms of learning and teaching and using the resultant data to improve our pedagogies and academic programs. The plan is designed to assess overall student achievement of the College’s general education core competencies (which include Inquiry and Problem-Solving, Global Learning,

and Integrative Learning which are expressed through three abilities: Oral, Written, and Digital Communication) as well as each major’s programmatic competencies. In line with our commitment to the academic, career, and personal growth and development of every student, the assessment system will use a variety of assessment tools to evaluate the effectiveness of learning and teaching. The Physical Sciences Program Director will monitor the quality of the program through the monitoring of students’ GPA to ensure students are maintaining the required grade point average. The minimum GPA requirement for the program is a 2.5 on a scale of 4.0.

For the Periodic Program Review (PPR) process, each program, led by faculty members, conducts a self- study, with student learning outcomes – along with a review of major issues and concerns (e.g., curriculum, facilities, student enrollment, retention, and graduation). The self-study report is reviewed by the Provost and Senior Vice President for Academic Affairs. A site visit by an external evaluator, often from a faculty at a college that we articulate the program with, reviews the report. The PPR involves a five-year time period which incorporates a planning year, an active review year, and three years of implementation. Note that implementation is based on faculty members’ recommendations which are data-informed findings, as uncovered through the PPR process, which are approved by the department chairperson and senior leaders within Academic Affairs.

The structure of the program ensures that students are prepared for successful transfer to York College. Therefore, the Physics and Chemistry chairpersons at YC and the Physical Sciences Program Director at LaGCC will meet every year to conduct an informal evaluation of the program. Course selection, transfer rates, transfer credits and student engagement/preparedness will also be reviewed.

References

1. LaGuardia Community College Institutional Profile 2017 http://www.laguardia.edu/IR/IR-facts/ 2. The STEM Workforce Challenge: The Role of the Public Work Force System in a National Solution for a Competitive STEM Workforce, U.S. Department of Labor Report, April 2007 3. Revisiting the STEM Workforce: A companion to Science and Engineering Indicators 2014, National Science Foundation, February 2015 4. Bureau of Labor Statistics, U.S. Department of Labor, Occupational Outlook Handbook, 2014-15 Edition, 5. STEM Work Force No More Diverse Than 14 Years Ago, Allie Bidwell, Us News & World Report Feb. 2015 6. Occupational Employment Statistics (OES) http://www.bls.gov/oes/ 7. Bureau of Labor Statistics, U.S. Department of Labor, The Economics Daily, 8.8 million science, technology, engineering, and mathematics (STEM) jobs in May 2016 on the Internet at https://www.bls.gov/opub/ted/2017/8-point-8-million-science-technology-engineering-and- mathematics-stem-jobs-in-may-2016.htm (visited November 11, 2017).

APPENDIX A COURSE DESCRIPTIONS FOR REQUIRED COURSES

Major Course Descriptions from the LaGuardia Community College Catalog for the Physical Science Major both Chemistry and Physics Track

NSF101-First Year Seminar- 2 cr., 2 Hours Lecture, 1Hour Studio Hour

This course provides information about the College’s policies and procedures, assists students with the process of self and career exploration, and introduces educational and career planning. All new students are required to register for New Student Seminar.

MAT 200- Precalculus I- 4 cr., 5 Hours

This course is intended as a preparation for the study of Calculus. Functions and their graphs will be analyzed theoretically within a framework that emphasizes their roles in applied settings. Particular attention will be placed on polynomial, exponential, logarithmic and trigonometric models. The use of graphing utilities as analytical tools will be emphasized. Each student is required to have a graphing calculator. Prerequisite: MAT115, COMPASS scores of 35 or higher on the Pre-algebra portion and 55 or higher on the Algebra portion.

MAT 201- Calculus I- 4 cr., 4 Hours

This course is the first of a three-course sequence designed to provide students with an appreciation of the usefulness and power of calculus. The course covers the fundamentals of the differential calculus of elementary functions and includes an introduction to integral calculus. Among the topics studied are limits, derivatives, applications of the derivative and integrals. Course Prerequisite- MAT 200

MAT 202- Calculus II- 4 cr., 4 Hours

This is a course designed to provide students with an appreciation of the usefulness and power of calculus. Emphasis will be placed on the application of calculus to various disciplines. Among the topics studied are the definite integral, area, formal integration, and applications of integration. Course Prerequisite- MAT 201

SCC 201- General Chemistry I-4 cr., 3 Hours Lecture, 3 Hours Lab

This is a two-semester sequence covering the basic concepts of chemistry and their historical development. The experimental nature of chemistry as well as the role of chemistry in many aspects of daily life are stressed. Among the topics studied are: SCC201: Atomic structure, chemical bonding, and chemical reactivity, quantitative relationships in chemical reactions, thermochemistry, and gases. SCC202: Liquids, solids, solutions, acid-base theory, chemical kinetics, chemical equilibrium, chemical thermodynamics, electrochemistry, nuclear chemistry. Course Prerequisite- MAT 115

SCC 202- General Chemistry II-4 cr., 3 Hours Lecture, 3 Hours Lab

SCP 231- General Physics I-4 cr., 4 Hours Lecture, 2 Hours Lab

This is the first part of a computer-based physics course intended for students who want to major in science, computer science or engineering. Computers will be used in the laboratory in conjunction with traditional equipment for problem solving, data collection, and analysis. Topics covered include vectors, Newton’s laws, equilibrium, rectilinear motion, two-dimensional motion, gravitation, Kepler’s laws, work and the work-energy theorem, rotational motion, simple harmonic motion, the physics of fluids, and heat. This course is the first course in a two course calculus-based physics sequence Course Prerequisite- MAT 201

SCC 232- General Physics II-4 cr., 4 Hours Lecture, 2Hours Lab

General Physics II, SCP232 This is the second part of a computer-based physics course intended for students who want to major in science, computer science, or engineering. Computers will be used in the laboratory in conjunction with traditional equipment for problem solving, data collection, and analysis. Topics covered include waves, wave motion, light propagation, geometrical optics, interference and diffraction, electric field and potential, capacitance and dielectrics, magnetic fields and forces, direct and alternating current, Ohm’s law, and electromagnetic waves. This course is the second course in a two course sequence. Course Prerequisite- MAT 202

SCP 210-Research Methods in Material Science-2 cr., 1 Hour Lecture, 2Hours Lab

This course provides students interested in scientific research the quantitative and analytical skills necessary to apply for research opportunities in material science. Students will attend talks by researchers in material science and carry out independent experimental projects to prepare them for work in a typical research setting. At the end of the course students will prepare a resume in consultation with faculty and visiting speakers in order to apply to summer programs in materials research. Course Prerequisite- One Science Lab course SCB201, SCC201, or SCP 231

Chemistry Track Courses

SCC 251- Organic Chemistry I-5 cr., 3 Hours Lecture, 4 Hours Lab

This course is a two-semester sequence emphasizing the synthesis, structure, reactivity and mechanisms of reaction of organic compounds. Laboratory stresses synthesis, purification, separation and identification of compounds. SCC251: Aliphatic and aromatic hydrocarbons; stereochemistry. Course Prerequisite- SCC 202

SCC 252- Organic Chemistry II-5 cr., 3 Hours Lecture, 4 Hours Lab

This course is a two-semester sequence emphasizing the synthesis, structure, reactivity and mechanisms of reaction of organic compounds. Laboratory stresses synthesis, purification, separation and identification of compounds. SCC252: Alcohols, ethers, aldehydes, ketones, carboxylic acids, amines, heterocycles, biomolecules. Course Prerequisite- SCC 251

MAT 203- Calculus III- 4 cr., 4 Hours

This is the third course in the calculus sequence and is designed to build upon the concepts and techniques of MAT201-202 and to provide a more rigorous conceptual grounding for the entire calculus sequence. Topics to be covered include limits and continuity, indeterminate forms, infinite series, the Taylor expansion and applications, solid geometry, the calculus of several variables, and an introduction to partial derivatives. Course Prerequisite- MAT 202

SCC 233- General Physics III-4 cr., 4 Hours Lecture, 2 Hours Lab

This is the second part of a computer-based physics course intended for students who want to major in science, computer science, or engineering. Computers will be used in the laboratory in conjunction with traditional equipment for problem solving, data collection, and analysis. Topics covered include waves, wave motion, light propagation, geometrical optics, interference and diffraction, electric field and potential, capacitance and dielectrics, magnetic fields and forces, direct and alternating current, Ohm’s law, and electromagnetic waves. This course is the second course in a two course sequence. Course Prerequisite- SCP 232

LaGuardia Community College The City University of New York

SCP 211: Research Methods in Physical Sciences

Academic Year:

Instructor: Dr. John Toland, Associate Professor Office: M210, Department of Natural Sciences Phone: 718-482-6005 Email: [email protected]

CLASS MEETINGS 3 Credits/4 Hours There will 2 hours of lectures per week There will be 2 hours of Laboratory every week

PRE-REQUISITE: SCC 201 or SCP 231; students must also have completed at least 30 credits in the Physical Sciences major.

COURSE DESCRIPTION: This course will serve as the capstone course for the physical sciences major for both the physics and chemistry tracks. The course will focus on developing the skills necessary to work in a physics or chemistry research lab. Students will attend talks by researchers in a physical science field such as chemistry or physics and carry out independent experimental projects to prepare them for work in a typical research setting. Students will work on guided and independent lab projects throughout the semester.

COURSE PURPOSE: This course serves as the capstone course for the physical sciences program currently being proposed at LaGuardia. This course will develop skills that will help students participate in faculty guided research both at LaGuardia and at their intended four-year school. This course will integrate the skills that students learned in their previous laboratory courses and apply them to more complex projects. The projects in this course require independent planning and can be executed in numerous ways. Students will develop their own executions and discuss their results in the form of an oral presentation and a poster presentation.

PERFORMANCE OBJECTIVES:

This course introduces students to topics and research methods in physics, chemistry, materials science, and electrical and mechanical engineering. Students will learn how to integrate experimental techniques with the knowledge obtained in chemistry and physics courses taken throughout their tenure at LaGuardia.

1. Students will learn how to identify pertinent ideas and central questions that arise during research seminars and presentations given by faculty and student researchers; 2. Students will become familiar with the academic research process in physical sciences and learn how to integrate experimental techniques they learned in chemistry and physics laboratories LaGuardia with research projects in this course and other laboratory settings;

3. Students will understand the differences between course work and work in research settings. Students will learn strategies for coming up with research methodologies to answer questions that commonly arise in research environments, effective time management, and ways to efficiently utilize resources, including faculty and graduate student mentors;

4. Students will understand the basic operating principles of instrumentation commonly found in physical sciences research labs and learn how to use these instruments to obtain data for subsequent analysis;

5. Students will create comprehensive procedures for laboratory projects that they develop that enables fellow students and researchers to recreate their experiments;

6. Students will demonstrate strategies for effectively communicating with faculty, graduate students and other staff researchers including the ability to respond to follow-up questions;

7. Students will create an electronic portfolio and a Curriculum Vitae that can be later utilized during applications and interviews and lead to further networking opportunities;

8. Students will understand the deliverables expected in a research lab and learn how to create these deliverables and revise them based on critical feedback.

COURSE POLICES

Academic Integrity Policy: Academic dishonesty is strictly prohibited in the City University of New York and is punishable by penalties ranging from a grade of “F” on a given test, research paper or assignment, to an “F” in the course, or suspension, or expulsion from the college. Please visit the following website to learn more about what is classified as academic dishonesty by CUNY. https://www.cuny.edu/about/administration/offices/la/Academic_Integrity_Policy.pdf

Attendance Policy: Attendance at all class sessions, lectures and laboratories, is essential for mastering the course material. TWO or more unexcused absences will result in failing grade in the course. Attendance will be taken during each class and laboratory session.

ASSIGNMENTS

Grading system: 3 Seminar Reflections & Summaries……………… 30% Laboratory Projects………….……………… 30% CV/Resume…………………………………. 10% Research Poster……………………………….. 15% Presentation……………………………………… 15%

Grading Schema: A = 93-100 % C+ = 77-79.9 % A- = 90-92.9 % C = 73-76.9 % C- = 70-72.9 % B+ = 87-89.9 % D+ = 67-69.9 % B = 84-86.9 % D = 63-66.9 % B- = 80-83.9 % D- = 60-62.9 %

F = less than 60%

E-Portfolio: Students will create and maintain an e-portfolio for this course. They will create a platform that highlights the skills they have developed and the projects they have worked on. The portfolio will be made available to research group leaders and seminar speakers in order to familiarize them with the students and to facilitate a seamless transition for the students into a subsequent research program.

Laboratory: The laboratory section of this course is comprised of several multi-week exercises in which students are introduces to the basics of operating common laboratory equipment, data collection and analysis using software platforms such as Mathematica and MatLab, and techniques for presenting their data and writing scientific reports using the LaTeX software environment. At the culmination of the laboratory students will be asked to create a research poster and oral presentation based on a project they carried out during the semester.

Reflections: Students will be required to write reflections on the seminar talks. These reflections will: • Identify the speaker and briefly summarize the talk • Identify interesting aspects of the talk • Identify something that was confusing / unclear aspects of the talk and ways to clarify these points • Discuss possible connections between the topic of the talk and student’s personal research interest • Indicate interest in performing further research in this topic

These reflections should not be written in a diary format. Students are asked to create a coherent narrative in answering these prompts keeping in mind that both the professor and other project managers will be looking at these responses.

Schedule and Assignments

Week Topic Reading/Assignments 1 Introduction Start Portfolio and CV 2 Speaker 1

Lab: Introduction to MatLab and LaTeX 3 Speaker 1 Discussion, Identifying Main Ideas and Questions.

Lab: Independent work 4 Speaker 2

Lab: RC Frequency Response 5 Speaker 2 Discussion, Creating a Procedure Reflection 1 Due

Lab: Independent Work RC Write up Due 6 How to write for the sciences and exploring the Reflection on Speaker 2 Due interdisciplinary experience

Lab: Optical Sources, Spectrometers, and Knife Edge Measurements 7 Speaker 3 CV Progress Report

Lab: Independent work 8 Discuss Speaker 3

Lab: Independent work 9 Speaker 4 Speaker Reflection 2 Due

Lab: Solar Cell’s with Organic dyes 10 Discuss Speaker4

Lab: Independent work 11 Speaker 5

Lab: Independent Work 12 Discuss Speaker 5 Turn in Research Poster, Turn in Final CV Lab: Independent work 13 Final Paper, Speaker 5 Reflection and Lab Reports Give oral presentations, Final Due Reflection Due

APPENDIX B FACULTY TEACHING ASSIGNMENTS (SED Form)

Appendix B: Full-Time Faculty Provide information on faculty members who are full-time at the institution and who will be teaching each course in the major field or graduate program. The application addendum for professional licensure, teacher certification, or educational leadership certification programs may provide additional directions for those types of proposals.

Miller, Dionne SCC 201 50% PhD CUNY Graduate Dr. Miller’s current research interests are in the optical SCC 202 School properties of nanoshell thin films and how the elucidation of these properties will influence the design of applications involving these particular nanoparticles. Dr. Miller is currently the coordinator of the chemistry program and also serves as the coordinator for SCC201: General Chemistry I. She also has developed and teaches a hybrid online course for SCC201.

Singh, Sunaina SCC 201 PhD Hunter College Professor Sunaina Singh was a research associate in Professor SCC 202 Graduate Center, CUNY Ronald Koder’s Physics lab. Dr. Singh’s research is on the SCC 251 synthesis of porphyrins bearing hydrogen bonding motifs to SCC 252 form supramolecular architectures which can be used for solar energy harnessing and energy channels for optical devices.

Mark, Kevin J SCC 201 50% PhD University of British Dr. Kevin Mark’s research focus is on the investigation of SCC 202 Columbia, Vancouver, BC, proteins, their non-covalent complexes and modifications using SCC 251 Canada mass spectrometry. He held a postdoctoral position at the Centre National de Recherche Scientifique in Orleans, France, and a Research Associate position at York College of CUNY in New York. Prior to joining LaGuardia, he was a substitute Assistant Professor at York College and a Visiting Assistant Professor at St. John’s University teaching undergraduate courses in general and analytical chemistry.

Provide information on faculty members who are full-time at the institution and who will be teaching each course in the major field or graduate program. The application addendum for professional licensure, teacher certification, or educational leadership certification programs may provide additional directions for those types of proposals.

Faculty Member Name and Program Percent Highest and Other Applicable Additional Qualifications: list related certifications/ licenses; occupational Title (include and identify Courses to be Time to Earned Degrees & Disciplines experience; scholarly contributions, etc. Program Director) Taught Program (include College/University) Sheffield, Allyson Anne SCP 231 50% PhD University of Virginia, Dr. Sheffield's doctoral thesis focused on the structure of the SCP 232 Charlottesville, VA Milky Way galaxy. Dr. Sheffield continues research in this area SCP 233 and she regularly travels to observatories in Arizona and Chile to collect spectroscopic data of old giant stars. Prior to joining the faculty at LaGuardia, Dr. Sheffield was a Visiting Professor in Physics and Astronomy at Vassar College in Poughkeepsie, NY, where she taught physics and astronomy courses at all levels. She carried out post-doctoral research as a Science Fellow at Columbia University from 2010-2014. At Columbia, Dr. Sheffield taught sections of the cross-disciplinary course Frontiers of Science. Gonzalez, Janet SCC 201 50% PhD City University of NY Dr. Gonzalez’s research involves the use of small organic SCC 202 molecules as a starting point for in silico and cellular SCC 251 approaches towards protein-ligand interactions concentrating SCC 252 on the serine protease, beta-lactamase expressed by Mycobacterium tuberculosis. Dr. Gonzalez has extensive teaching experience and has taught at Lehman College, Bronx Community College and The Sophie Davis School of Biomedical Education. She is currently the coordinator of the Organic Chemistry program.

Faculty Member Name and Program Percent Highest and Other Applicable Additional Qualifications: list related certifications/ licenses; occupational Title (include and identify Courses to be Time to Earned Degrees & Disciplines experience; scholarly contributions, etc. Program Director) Taught Program (include College/University) Toland, John SCP 231 50% PhD Stevens Institute of Dr. Toland’s thesis research centered on modeling the Program Director SCP 232 Technology, Hoboken New rotational sensitivity of multiple interferometers connected in SCP 233 Jersey series. Dr. Toland explored the effects of size varying SCP 210 interferometers both due to random imperfections and chains with designed size variations. Dr. Toland received his Bachelor’s degree in physics form Humboldt State University (Arcata, CA) in 2005. Dr. Toland continues his research into simulating the transmission properties of multiple interferometers to determine how their geometry affects their rotational sensitivity. He is extending this work to explore the effects due to other geometric phase shifts subject to interferometer geometries studied with phase shifts associated with rotation. Dr. Toland is also exploring the possibilities of incorporating heavily parallel processer computation into his work; he is part of an ongoing interdisciplinary collaboration at Stevens Institute of Technology that has been recognized by NVIDA as a research center for parallel processing using the CUDA programming language.

Dr. Toland has been teaching physics courses at LaGuardia since the Fall of 2012 and previously taught physics course ate CUNY: Kingsborough Community College and at Stevens instate of Technology. He has experience teaching both algebra and calculus based general physics courses and laboratories.

Dr. Gao also has rich research experience in experimental physics, more specifically, studying, developing and using new kinds of organic polymers doped with rare earth complex solutions as the emitting material in electroluminescent devices. This research work involved thin-film preparation using vapor deposition and spin coating techniques. It also required sophisticated skills in acquiring and analyzing the fluorescence and absorption spectra of the rare earth complex solutions and the light-emitting Diode (LED) devices made with them. Her work in this area has been published in different peer-reviewed journals.

Dr. Gao also has significant experience in college teaching. Before joining the Natural Sciences Department at LaGuardia Community College, she taught diverse courses as Adjunct Assistant Professor at Baruch College, Fashion Institute of Technology and BMCC for several years.

Faculty Member Name and Program Percent Highest and Other Applicable Additional Qualifications: list related certifications/ licenses; occupational Title (include and identify Courses to be Time to Earned Degrees & Disciplines experience; scholarly contributions, etc. Program Director) Taught Program (include College/University) Senkov, Roman SCP 231 50% Budker Institute of Some of Dr. Senkov's recent works are devoted to accurate SCP 232 Nuclear Physics, calculation of nuclear level densities (NLD). Spin- and parity- SCP 233 Novosibirsk, Russia dependent nuclear level densities represent an important ingredient for theory of nuclear reactions. Knowledge of the rates of certain nuclear reactions are extremely important for applications to nuclear astrophysics, where these rates determine formation, evolution, and the main properties of stars. In most of the cases relevant to nuclear astrophysics, where experimental information is not available, the reaction rates for medium and heavy nuclei can only be estimated theoretically. Dr. Senkov wrote two large-scale FORTRAN codes for calculations of NLD which allow for parallelization of computing. These codes were successfully tested on up to 6,000 processors (Phys. Lett. B702, 413 (2011), Comp. Phys. Comm. 184, 215 (2013)). The new algorithm Dr. Senkov developed for this project is about several thousand times faster compared to the previous one. Using Dr. Senkov's codes it became possible to calculate nuclear level densities for given spin and parity for practically any available Shell Model Hamiltonians and model spaces avoiding direct diagonalization. This provided a breakthrough in the methods for NLD calculations.

Kowalczyk, Marta SCC201 50% PhD City University of NY Before joining LaGCC, Dr. Kowalczyk taught undergraduate SCC202 chemistry classes at Queens College (CUNY). She also worked as a postdoctoral research associate at Brookhaven National Laboratory. Dr. Kowalczyk has seven publications, thirteen presentations at the (inter)national conferences, and her research focuses on the photochemistry of inorganic and organic compounds, charge transfer mechanisms, artificial photosynthesis, and conjugated polymers for use in photovoltaic cells, and energy storage.

APPENDIX C FINANCIAL TABLES (CUNY)

The Five-Year Revenue Projections for Program COMMUNITY COLLEGE WORKSHEET Year 1 = Fall 2014 Year Year Year Year Year EXISTING FULL-TIME STUDENTS One Two Three Four Five Tuition & Fees: # of EXISTING FULL-TIME, In-State Students (linked from "Enroll & Seat Need Projections") 20 15 15 15 15 Tuition Income (calculates 2% increase per year after Fall 2015) $4,500 $4,800 $4,896 $4,994 $5,094 Total Tuition $90,000 $72,000 $73,440 $74,909 $76,407 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total In-State Tuition & Fees $90,000 $72,000 $73,440 $74,909 $76,407

Tuition & Fees: # of EXISTING FULL-TIME, Out-of-State Students (linked from "Enroll & Seat Need Projections") 0 0 0 0 0 Annual Avg # of Credits per FT student (24-30) Tuition Income (Specify Rate per credit. Calculates 2% annual increase after Fall 2015) $290 $305 $311 $317 $324 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total Out-of-State Tuition & Fees $0 $0 $0 $0 $0

TOTAL EXISTING FULL-TIME TUITION REVENUE $90,000 $72,000 $73,440 $74,909 $76,407

Year Year Year Year Year EXISTING PART-TIME STUDENTS One Two Three Four Five

Tuition & Fees: # of EXISTING PART-TIME, In-State Students (linked from "Enroll & Seat Need Projections") 5 5 5 5 5 Total Enrolled Credits (Enter Avg # credits per student per year-Fall+ Spring+Summer -- i.e. 6 Fall, 6 Spring, 3 Summer=15) Tuition Income (Specify Rate per credit. Calculates 2% increase per year after Fall 2015) $185 $195 $199 $203 $207 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total In-State Tuition & Fees $0 $0 $0 $0 $0

Tuition & Fees: # of EXISTING PART-TIME Out of State Students (linked from "Enrollment and Seat Need Projections") 0 0 0 0 0 Total Enrolled Credits (Enter Avg # credits per student per year-Fall+ Spring+Summer -- i.e. 6 Fall, 6 Spring, 3 Summer=15) Tuition Income (Specify Rate per credit. Calculates 2% increase per year after Fall 2015) $290 $305 $311 $317 $324 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 Total Out-of-State Tuition & Fees $0 $0 $0 $0 $0

TOTAL EXISTING PART TIME REVENUE $0 $0 $0 $0 $0

TOTAL EXISTING REVENUE (LINKS TO REVENUE SPREADSHEET ROW 5) $90,000 $72,000 $73,440 $74,909 $76,407

Year Year Year Year Year NEW FULL-TIME STUDENTS One Two Three Four Five Tuition & Fees: # of NEW FULL-TIME, In-State Students (linked from "Enroll & Seat Need Projections") 20 30 40 50 50 Tuition Income (Calculates 2% increase per year after Fall 2015) $4,500 $4,800 $4,896 $4,994 $5,094 Total Tuition $90,000 $144,000 $195,840 $249,696 $254,690 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total In-State Tuition & Fees $90,000 $144,000 $195,840 $249,696 $254,690

Tuition & Fees: # of NEW FULL-TIME, Out-of -State Students (linked from "Enroll & Seat Need Projections") 0 0 0 0 0 Annual Avg # of Credits per FT student (24-30) Tuition Income (Specify Rate per credit. Calculates 2% increase per year after Fall 2015) $290 $305 $311 $317 $324 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total Out-of-State Tuition & Fees $0 $0 $0 $0 $0

TOTAL NEW FULL-TIME TUITION REVENUE $90,000 $144,000 $195,840 $249,696 $254,690

Year Year Year Year Year NEW PART-TIME STUDENTS One Two Three Four Five Tuition & Fees:

# of NEW PART-TIME, In-State Students (linked from "Enroll & Seat Need Projections") 5 5 5 10 10 Total Enrolled Credits (Enter Avg # credits per student per year-Fall+ Spring+Summer -- i.e. 6 Fall, 6 Spring, 3 Summer=15) Tuition Income (Specify Rate per credit. Calculates 2% increase per year after Fall 2015) $185 $195 $199 $203 $207 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total In-State Tuition & Fees $0 $0 $0 $0 $0

Tuition & Fees: # of NEW PART-TIME, Out-of-State Students 0 0 0 0 0 Total Enrolled Credits (Enter Avg # credits per student per year-Fall+ Spring+Summer -- i.e. 6 Fall, 6 Spring, 3 Summer=15) Tuition Income (Specify Rate per credit) calculates 2% increase per year $290 $305 $311 $317 $324 Total Tuition $0 $0 $0 $0 $0 Student Fees (enter ANNUAL program fees other than standard CUNY fees) Total Fees 0 0 0 0 0 Total Out-of-State Tuition & Fees $0 $0 $0 $0 $0

TOTAL NEW PART-TIME REVENUE $0 $0 $0 $0 $0

TOTAL NEW REVENUE (LINKS TO REVENUE SPREADSHEET ROW 7) $90,000 $144,000 $195,840 $249,696 $254,690

Year Year Year Year Year STATE REVENUE One Two Three Four Five # EXISTING FTEs 20 15 15 15 15 Appropriaton per FTE $2,422 $2,422 $2,422 $2,422 $2,422 STATE REVENUE FROM EXISTING SOURCES -LINKS TO REVENUE SPREADSHEET ROW 9 $48,440 $36,330 $36,330 $36,330 $36,330

# NEW FTEs 20 30 40 50 50 Appropriaton per FTE $2,422 $2,422 $2,422 $2,422 $2,422 STATE REVENUE FROM NEW SOURCES -LINKS TO REVENUE SPREADSHEET ROW 11 $48,440 $72,660 $96,880 $121,100 $121,100

Year Year Year Year Year OTHER REVENUE One Two Three Four Five Other Revenue From Existing Sources (specify and explain)- LINKS TO REVENUE SPREADSHEET ROW 13) Other Revenue New (specify and explain) (LINKS TO REVENUE SPREADSHEET ROW 15)

Projected Revenue Related to the Proposed Program 1st Year 2nd Year 3rd Year 4th Year 5th Year

† † † † Revenues[1] Academic Year[2] Academic Year Academic Year Academic Year Academic Year

Tuition Revenue[3]

01. From Existing Sources[4] $90,000 $72,000 $73,440 $74,909 $76,407

02. From New Sources[5] $90,000 $144,000 $195,840 $249,696 $254,690 03. Total $180,000 $216,000 $269,280 $324,605 $331,097

State Revenue[6] $48,440 $36,330 $36,330 $36,330 $36,330

04. From Existing Sources§ 05. From New Sources** $48,440 $72,660 $96,880 $121,100 $121,100 06. Total $96,880 $108,990 $133,210 $157,430 $157,430

Other Revenue[7] 07. From Existing Sources§ $0 $0 $0 $0 $0 08. From New Sources** $0 $0 $0 $0 $0 09. Total $0 $0 $0 $0 $0

Grand Total[8] 10. From Existing Sources§ $138,440 $108,330 $109,770 $111,239 $112,737 11. From New Sources** $138,440 $216,660 $292,720 $370,796 $375,790 TOTAL $276,880 $324,990 $402,490 $482,035 $488,527

[1] Specify the inflation rate used for projections. [2] Specify the academic year. [3] Please explain how tuition revenue was calculated. [4] Existing sources means revenue generated by continuing students. Please rember to account for attrition and graduation rates

[5] New sources means revenue engendered by new students. The revenue from new sources from one year should be carried over to the next year as revenues from continuing sources with adjustments for inflation.

[6] Public institutions should include here regular State appropriations applied to the program. [7] Specify what is included in "other" category.

[8] Enter total of Tuition, State and Other Revenue, from Existing or New Sources.

The Five-Year Financial Projections for Program

DIRECT OPERATING EXPENSES Year 1 Year 2 Year 3 Year 4 Year 5

Include additional expenses incurred by other programs when satisfying needs of new program. Faculty need should be commensurate with "net section needs" based on enrollment (see "Enroll & Seat Need Projections" tab)

Current Full Time Faculty Overload (include Summer) New Full Time Faculty Base Salary (list separetely) New Full Time Faculty Overload (include Summer) New Faculty Re-assigned Time (list seperately) Full Time Employee Fringe Benefits (41.6%) 0 0 0 0 0 Total (Links to Full-Time Faculty on Program Exp $ $ $ $ $ Worksheet) - - - - - Part Time Faculty Actual Salaries Part Time Faculty Actual Fringe Benefits (24.3%) 0 0 0 0 0 Total (Links to Part-Time Faculty Program Exp $ $ $ $ $ Worksheet) - - - - -

Full Time Staff Base Salary (list separetely) Full Time Staff Fringe Benefits (41.6%) 0 0 0 0 0 Total (Links to Full-Time Staff on Program Exp $ $ $ $ $ Worksheet) - - - - - Year 1 Year 2 Year 3 Year 4 Year 5 PART-TIME STAFF (do not include library staff in this section) Part Time Staff Base Salary (list separately) 0 0 0 0 0 Faculty Replacement Costs (replacement of full-time faculty - e.g. on release time - with part-time faculty) 0 0 0 0 0 Graduate Assistants 0 0 0 0 0 Student Hourly 0 0 0 0 0 Part Time Employee Fringe Benefits (24.3%) 0 0 0 0 0

Total (Links to Part-Time Staff on Program Exp $ $ $ $ $ Worksheet) - - - - -

LIBRARY Library Resources 500 500 250 250 250 Library Staff Full Time (List Separately) Full Time Staff Fringe Benefits (41.6%) 0 0 0 0 0 Library Staff Part Time (List Separately) Part Time Employee Fringe Benefits (24.3%) 0 0 0 0 0 TOTAL (Links to Library on Program Exp $ $ $ $ $ Worksheet) 500.00 500.00 250.00 250.00 250.00

EQUIPMENT Computer Hardware 1000 1000 500 500 500 Office Furniture Other (Specify) Total (Links to Equipment on Program Exp $ $ $ $ $ Worksheet) 1,000.00 1,000.00 500.00 500.00 500.00

LABORATORIES Laboratory Equipment 0 0 0 0 0 Other (list separately) 0 0 0 0 0 TOTAL (Links to Laboratories on Program Exp $ $ $ $ $ Worksheet) - - - - -

Year 1 Year 2 Year 3 Year 4 Year 5 SUPPLIES AND EXPENSES (OTPS) Consultants and Honoraria Office Supplies

Instructional Supplies Faculty Development Travel and Conferences Membership Fees Advertising and Promotion Accreditation Computer Software Computer License Fees Computer Repair and Maintenance Equipment Repair and Maintenance New Total Supplies and OTPS Expenses (Links to $ $ $ $ $ Supplies on Program Exp Worksheet) - - - - -

CAPITAL EXPENDITURES Facility Renovations Classroom Equipment Other (list separately) TOTAL (Links to Capital Expenditures on Program $ $ $ $ $ Exp Worksheet) - - - - -

Other (list separately) $ $ $ $ $ TOTAL (Links to Other on Program Exp Worksheet) - - - - -

Enroll & Seat Need Projections 2019-2020 2020-2021 2021-2022 2022-2023 2023-2024 Projected Enrollment Year One Year Two Year Three Year Four Year Five Existing Full-time Students 20 15 15 15 15 In-State 20 15 15 15 15

Out-of-State - Existing Full-time Total 20 15 15 15 15

Existing Part-time Students 5 5 5 5 5 In-State 5 5 5 5 5 Out-of-State Existing Part-time Total 5 5 5 5 5

New Full-time Students 20 30 40 50 50 In-State 20 30 40 50 50 Out-of-State NEW Full-time Total 20 30 40 50 50

New Part-time Students 5 5 5 10 10 In-State 5 5 5 10 10 Out-of-State New Part-time Total 5 5 5 10 10

NOTES: New students are students who would not otherwise have be enrolled in your college if this program were not offered. The proposal text should explain the basis for this enrollment estimate.

Existing Students are students currently enrolled in another program at your college, or students who would have enrolled in another program at your college, had the new program not been established.

Section Seats per Student Year One Year Two Year Three Year Four Year Five Full-time Students Existing Courses 10 8 10 10 10 New Courses 0 2 0 0 0 Total (normally equals 10)

Part-Time Students Existing Courses 4 2 4 4 4 New Courses 0 2 0 0 0 Total (normally equals 4-6)

Seat & Section Needs Year One Year Two Year Three Year Four Year Five Seat Need for Existing Students Existing Courses 130 170 170 170 New Courses 40 - - -

Seat Need for New Students Existing Courses 250 420 540 540 New Courses 70 - - -

Total Seat Need Existing Courses 380 590 710 710 Avail. Seats in Existing Courses Net Seat Need in Existing 380 590 710 710 New Courses 110 - - - All Courses 490 590 710 710

APPENDIX D ARTICULATION AGREEMENT(S) (CUNY)

ARTICULATION AGREEMENT FORM

A. SENDING AND RECEIVING INSTITUTIONS

Sending College: Fiorello H. LaGuardia Community College Department: Natural Sciences Program: Physical Science (Physics Track) Degree: A.S. in Physical Science: Physics

Receiving College: York College Department: Department of Earth and Physical Sciences Program: Physics Degree: B.S. Physics

B. ADMISSION REQUIREMENTS FOR SENIOR COLLEGE PROGRAM (e.g., minimum GPA, audition/portfolio)

• Junior Standing • Overall GPA of at least 2.0 on a 4.0 scale • Grade of C or better in any major course to be accepted for transfer. • Any major course with a grade of C- or less must be repeated to earn a grade of C or better (limited to one opportunity to repeat a course). • Grade of C or higher in a credit bearing mathematics course with three or more credits* • Grade of C or better in freshman composition, its equivalent, or a high level English course* • York College will accept transfer credit only, not course grades. At most 68 semester credits can be accepted towards the York College B.S. degree in Physics. • Students eligible for transfer to York College under this agreement must have met at least the minimum requirements for admission to LAGCC, including a US high school diploma or its equivalent. • During the period of this agreement, both institutions agree: • A) To monitor the academic performance of LAGCC students who wish to matriculate at York College under this agreement, identify problems, and work cooperatively to ensure smooth transfer with minimal academic disruption. • B) To notify each other concerning any contemplated the curricular changes, which would affect the future of this agreement.

Total transfer credits toward the baccalaureate degree: 60

Total additional credits required at the senior college to complete baccalaureate degree: 60

C. COURSE TO COURSE EOUIVALENCIES AND TRANSFER CREDIT AWARDED LaGuardia Community College York College Course Number & Title Credits Course Number & Title Credits Credits Awarded Required Core ENGL 101 English Composition I 3 ENG 125 English Composition I: 3 3 Introduction to College Writing ENGL 102 English Composition II: 3 ENG 126 English Composition II: 3 3 Introduction to Literature Writing MAT 115 College Algebra 3 MATH 104 College Algebra 3 3 Life & Physical Science course 3 Life & Physical Science course 3 3 Subtotal 9 Subtotal 9 Flexible Core World Cultures & Global Issues course 3 World Cultures & Global Issues 3 3 course U.S. Experience In Its Diversity 3 U.S.Experience In Its Diversity 3 3 course course Creative Expression course 3 Creative Expression course 3 3 Individual and Society course 3 Individual and Society course 3 3 Scientific World course: Scientific World SCC 201General Chemistry I 4 CHEM 108 Principles of 3 3 Chemistry I CHEM 109 1 1 Principles of Chemistry I Scientific World course: Scientific World SCP 231General Physics I 4 PHYS 117 University Physics I 3 3 PHYS 113 Physics Laboratory I 1 1 Subtotal 20 Subtotal 20 Pathways Total 29 Pathways Total 29

Specific Program Course Requirements (lncluding Prerequisites) NSF 100 Natural Sciences First 2 ELEC 1000 Elective credit 2 2 Year Seminar MAT 200 Pre-Calculus 4 MAT 120 Pre-Calculus 4 4 MAT 201Calculus I 4 MATH 121 Analytical Geometry & 4 4 MAT 202 Calculus II 4 MATH 122 Analytical Geometry & 4 4 Calculus MAT 203 Calculus III 4 MATH 221 Analytical Geometry & 4 4 Calculus SCP 232 General Physics II 4 PHYS 118 University Physics II 3 3 PHYS 114 Physics Laboratory II 1 1 SCP 233 Modern Physics 4 PHYS 241 Modern Physics 3 3 PHYS 999 Physics blanket credit 1 1 Free Elective* 2 Free elective 2 2 SCP 211 Research Methods in Physical 3 PHYS 999 Physics blanket credit 3 3 Science (Capstone) Subtotal 31 Subtotal 31 Total for AS degree 60 Total for 60 *Recommended to take SCC 202 4 credits

D. SENIOR COLLEGE UPPER DIVISION COURSES REMAINING FOR BACCALAUREATE DEGREE

General Education Requirements (College Option) Course Credits Writing Intensive course at the 200-level or higher: 0 (Fulfilled by PHYS 383 below)

Writing Intensive course at the 200-level or higher OR WRIT 300-level course: 3 Subtotal 3

Major Discipline Course Credits CHEM 111 Principles of Chemistry II 3.5 CHEM 112 Principles of Chemistry II Laboratory 1.5 MATH 223 Differential Equations and Dynamical Systems 3 MATH 333 Linear Algebra 4 PHYS 231Oscillations and Waves 3 PHYS 312 Classical Mechanics I 3 PHYS 321 Electricity and Magnetism I 3 PHYS 351Statistical Mechanics 3 PHYS 383 Modem Physics Laboratory (Writing Intensive) 3 PHYS 441Quantum Mechanics and Atomic Physics 3 Subtotal 30 Major Electives Choose two courses at 400-level or higher in PHYS or ASTR 6 Subtotal 6 Free Electives 21 Total Credits Required for B.S. in Physics: 120

E. ARTICULATION AGREEMENT FOLLOW-UP PROCEDURES

Procedures for reviewing updating, modifying or terminating agreement: • When any of the programs undergo any changes relevant to this agreement, this articulation agreement will be reviewed and revised as necessary by one or two faculty members of each institution's department, selected by their respective Chairpersons to represent them.

• At the end of the academic year, the various representatives of each institution as indicated above will review the performance of transfer students to determine if adjustment to, or termination of the articulation agreement, is needed.

This articulation agreement will be publicized on both the LaGuardia Community College and York College websites. Transfer advisers at LAGCC will promote this agreement with eligible students. The faculty representative from York College's B.S in Physics will arrange an annual information session with the LAGCC campus for interested students.

ADVISOR RECOMMENDATIONS: LaGuardia Community College students who plan to transfer into the Bachelors of Science degree in Physics program at York College are advised to strictly follow the Program Requirements and Program Electives listed in the proposal to satisfy the requirements for the Associates of Science degree in Physical Science (Physics Track) at LAGCC and to ensure that the maximum number of credits and required coursework are transferred to satisfy the physics program requirements at York College. Refer to both college websites for a list of pathways general education requirements for both the A.S and B.S physics programs.

F. ADDITIONAL INFORMATION:

PURPOSE: York College and LaGuardia Community College hereby enter into this agreement is to facilitate the opportunity for students who wish to transfer from LaGuardia Community College's Associate Degree in Physical Science (Physics Track) to York College's Bachelor of Science Degree in Physics. The attached sections A, B. and C, of this agreement specify the conditions and requirements for admittance to York College.

CHANGES: Neither party may change this agreement unilaterally. Proposed changes in policies and curricula (i.e. admission, curriculum, and degree requirements, course numbers, course content, and/or catalog descriptions by either party), must be communicated in writing to the other party, and jointly agreed upon in consultation with the relevant officials of each institution. Any changes agreed- upon must be signed, dated, and attached to this original agreement. It is highly recommended that the department chairs from the respective college programs jointly complete sections A, B, and C of this agreement at least every two years.

NOTICE OF CANCELLATION: Either party may independently cancel this agreement by notifying the other party no less than one academic year before the intended date of cancellation.

York College agrees to accept into the Bachelors of Science degree program in Physics students from LaGuardia community college who successfully complete the Physical Science (Physics Track) curriculum and degree requirements described in section A, B, and C of this agreement; thereby receiving an Associates of Science degree.

Effective Date:

Dr. Paul Arcario Dr. Panayiotis Meleties Provost and Vice President for Academic Provost and Sr. Vice President for Academic Affairs Affairs Fiorello H. LaGuardia Community College York College, CUNY

Dr. Dionne Miller, Chair Dr. Timothy Paglione, Chair Natural Sciences Department Department of Earth & Physical Sciences Fiorello H. LaGuardia Community College York College, CUNY

ARTICULATION AGREEMENT FORM

A. SENDING AND RECEIVING INSTITUTIONS

Sending College: Fiorello H. LaGuardia Community College Department: Natural Sciences Program: Physical Science (Chemistry Track) Degree: A.S. in Physical Science: Chemistry

Receiving College: York College Department: Department of Chemistry Program: Chemistry Degree: B.S. Chemistry (Track 1)

B. ADMISSION REQUIREMENTS FOR SENIOR COLLEGE PROGRAM (e.g., minimum GPA, audition/portfolio)

• Junior Standing • Overall GPA of at least 2.0 on a 4.0 scale • Grade of C or better in any major course to be accepted for transfer. • Any major course with a grade of C- or less must be repeated to earn a grade of C or better (limited to one opportunity to repeat a course). • Grade of C or higher in a credit bearing mathematics course with three or more credits* • Grade of C or better in freshman composition it's equivalent or a higher level English course* • York College will accept transfer credit only, not course grades. At most 60 semester credits will be excepted towards the York College B.S. degree in chemistry • Students eligible for transfer to York College under this agreement must have met at least the minimum requirements for admission to LAGCC, including a US high school diploma or its equivalent. • During the period of this agreement, both institutions agree: . A) To monitor the academic performance of LAGCC students who wish to matriculate at York College under this agreement, identify problems, and work cooperatively to ensure smooth transfer with minimal academic disruption. . B) To notify each other concerning any contemplated the curricular changes, which would affect the future of this agreement.

Total transfer credits toward the baccalaureate degree: 60 Total additional credits required at the senior college to complete baccalaureate degree: 60

C. COURSE TO COURSE EQUIVALENCIES AND TRANSFER CREDIT AWARDED Sending College Receiving College Equivalent Credit Granted Course and Title Cr. Course and Title Cr. General Education (Liberal Arts, Core Distribution) Courses

Math and Quantitative Reasoning: 3 Math and Quantitative Reasoning: 3 3 MAT 115 College Algebra MATH 104 College Algebra English Composition: 3 English Composition: 3 3 ENG 101 Composition I ENGL 125 Composition I English Composition: 3 English Composition: 3 3 ENG 102 Writing through Literature ENGL 126 Composition II World Cultures and Global Issues 3 World Cultures and Global Issues 3 3 Creative Expression 3 Creative Expression 3 3 Individual and Society 3 Individual and Society 3 3 US Experience in its Diversity 3 US Experience in its Diversity 3 3 Scientific World : 4 Scientific World : SCC201 General Chemistry I CHEM 108 Principles of Chemistry I 3.5 3 CHEM 109 Principles of Chemistry I 1.5 1 Lab MAT200 PreCalculus 4 MAT 120 PreCalculus 4 4 SCC202 General Chemistry II 4 CHEM111 Principles of Chemistry II 3.5 3 CHEM112 Principles of Chemistry II 1.5 1 Lab

SUBTOTAL 33 Specific Program Requirements (Including Prerequisites) NSF 100 2 ELEC 1000 Elective credit 2 2 MAT201 Calculus I 4 MATH 121 Analytical Geometry & 4 4 Calculus I MAT202 Calculus II 4 MATH 122 Analytical Geometry & 4 4 Calculus II SCC251 Organic Chemistry I 5 CHEM 231 Organic Chemistry I/ 3 3 CHEM 232 Techniques of Organic 2 2 Chemistry 1 SCC252 Organic Chemistry II 5 CHEM 233 Organic Chemistry II/ 3 3 CHEM 234 Techniques of Organic 2 2 Chemistry II Life and Physical Sciences: 4 Life and Physical Sciences: SCP231 General Physics I PHYS 117 University Physics I 4 3 PHYS 113 Physics Laboratory I 1 1 SCP 211 Research Methods in 3 CHEM 490 Independent Study 3 3 Physical Science (Capstone)

SUBTOTAL 27

TOTAL = 60

D. SENIOR COLLEGE UPPER DIVISION COURSES REMAINING FOR BACCALAUREATE DEGREE

Course and Title Credits General Education (Liberal Arts, Core Distribution) and Other Required Courses College Option Writing Intensive (WI) course I of 2 at the 200-level or Satisfied below by CHEM higher 421. Writing Intensive (WI) course 2 of 2 at the 200-level or higher OR WRIT 0-3 (not required if CHEM 302 Research and Writing for the Sciences, Math, and Technology 462 below is taken) Free Elective 21-24 Subtotal 24

Prerequisite and Major Courses CHEM 310 Inorganic Chemistry 3 CHEM 321 Physical Chemistry-Thermodynamics 3 CHEM 322 Physical Chemistry-Quantum Chemistry 3 CHEM 330 Structure and Mechanism in Biochemistry 3 CHEM 341 Instrumental Analysis I 3 CHEM 342 Instrumental Analysis II 3 CHEM 421 Physical-Inorganic Laboratory 3 MATH 221 Analytic Geometry and Calculus III 4 PHYS 118 University Physics II 4 PHYS 114 Physics Laboratory II 1 Choose 2 electives from the following lists: 6 CHEM 339 Heterocyclic Chemistry and Drug Chemistry CHEM 450 Advanced Topics in Chemistry CHEM 460 Biochemistry I CHEM 462 Experiments in Biological Chemistry (WI) CHEM 490 Independent Study I Subtotal 36 TOTAL 60

E. ARTICULATION AGREEMENT FOLLOW-UP PROCEDURES

Procedures for reviewing, updating, modifying or terminating agreement: • When any of the programs undergo any changes relevant to this agreement, this articulation agreement will be reviewed and revised as necessary by one or two faculty members of each institution’s department, selected by their respective Chairpersons to represent them.

• At the end of academic year the various representatives of each institution as indicated above will review the performance of transfer students to determine if adjustment to, or termination of the articulation agreement, is needed.

This articulation agreement will be publicized on both the LaGuardia Community College and York College websites. Transfer advisers at LAGCC will promote this agreement with eligible students. The faculty representative from York College’s B.S in Chemistry will arrange an annual information session with the LAGCC campus for interested students.

ADVISOR RECOMMENDATIONS: LaGuardia Community College students who plan to transfer into the Bachelors of Science degree in Chemistry program at York College are advised to strictly follow the Program Requirements and Program Electives listed in the proposal to satisfy the requirements for the Associates of Science degree in Physical Sciences (Chemistry Track) at LAGCC and to ensure that the maximum number of credits and required coursework are transferred to satisfy the chemistry program requirements at York College. Refer to both college websites for a list of pathways general education requirements for both the A.S and B.S chemistry programs.

F. ADDITIONAL INFORMATION:

PURPOSE: York College and LaGuardia Community College hereby enter into this agreement is to facilitate the opportunity for students who wish to transfer from LaGuardia Community College’s Associate Degree in Physical Science Chemistry Track to York College’s Bachelor of Science Degree in Chemistry. The attached sections A, B, and C, of this agreement specify the conditions and requirements for admittance to York College.

CHANGES: Neither party may change this agreement unilaterally. Proposed changes in policies and curricula (i.e. admission, curriculum, and degree requirements, course numbers, course content, and/or catalog descriptions by either party), must be communicated in writing to the other party, and jointly agreed upon in consultation with the relevant officials of each institution. Any changes agreed-upon must be signed, dated, and attached to this original agreement. It is highly recommended that the department chairs from the respective college programs jointly complete sections A, B, and C of this agreement at least every two years.

NOTICE OF CANCELLATION: Either party may independently cancel this agreement by notifying the other party no less than one academic year before the intended date of cancellation.

York College agrees to accept into the Bachelors of Science degree program in Chemistry students from LaGuardia community college who successfully complete the Physical Sciences Chemistry track curriculum and degree requirements described in section A, B, and C of this agreement; thereby receiving an Associates of Science degree .

Effective Date:

Dr. Paul Arcario Provost and Vice President for Academic Affairs Fiorello H. LaGuardia Community College

Dr. Dionne Miller, Chair Natural Sciences Department Fiorello H. LaGuardia Community College

Dr. Panayiotis Meleties Provost and Sr. Vice President for Academic Affairs York College, CUNY

Dr. Ruel Z. B. Desamero, Chair Department of Chemistry York College, CUNY

Appendix E General Information (SED Form)

General Information

Institution (Legal Name) Institution Code

LaGuardia Community College 372000

Proposed Program Title Degree Award

Physical Sciences AS Address of Any Campus Where the Proposed Program Will Be Offered Full-time or Part-time 3 (main and/or branch campuses) 31-10 Thompson Avenue, Long Island City, NY 11101 both All Program Format(s) HEGIS Code (standard, distance education4, evening, weekend and/or other) Standard, evening 5649

Joint Registration IHE (if applicable) Total Number of Credits

Lead Contact [First Name, Last Name, Title] Telephone Number

Dione Miller, Interim Associate Dean for Academic Affairs 718-482-5741

Email Address

3 Please refer to §52.2(c) and §145-2.1 of the Regulations of the Commissioner for definitions and information concerning full and part time study. Note: Only programs registered as full time are eligible for TAP. Programs are subject to audit by the NYS Office of the State Comptroller and the Higher Education Services Corporation (HESC) for financial aid compliance purposes. 4 If a major portion of the program (50% or more) can be completed through study delivered by distance education then the program must be registered in the distance education format. Hybrid or blended courses do not count toward the 50%.

I.B.3 – CITY COLLEGE—BS IN SCIENCE LEARNING AND PUBLIC ENGAGEMENT

WHEREAS, the program is designed to prepare students for 21st century knowledge-based careers and equip graduates with the skills required to deliver STEM learning for science education careers in a variety of non-classroom environments such as museums, botanical gardens, zoos and environmental non-profits, and

WHEREAS, the program emphasizes the development of skills required for public outreach and community education initiatives that address educational pedagogy, communications media, and non-profit program management, and

WHEREAS, the applied courses in the program will be coupled with content area courses aimed at developing a broad-based science expertise, and

WHEREAS, the proposed program will require students to participate in field experiences that includes two internships, which will develop their potential to engage a diverse audience of science learners and will bolster their qualifications for full-time entry-level positions in the field, and

WHEREAS, City College partners with such organizations as the American Museum of Natural History, the Brooklyn Botanical Garden, the Bronx Zoo/Wildlife Conservation Society, the New York Botanical Garden and the New York Hall of Science, and

WHEREAS, this will be the only degree program in New York City specifically designed to prepare undergraduates for careers at informal science learning environments, an expanding field, according to the National Research Council of the National Academy of Sciences and the U.S. Department of Labor, and

WHEREAS, an articulation agreement allowing for seamless student transfer has been signed with Bronx Community College, with additional articulation agreements in process, so be it

RESOLVED, that the program in Science Learning and Public Engagement at City College, leading to the Bachelor of Science, be approved effective May 8, 2019, subject to financial ability.

EXPLANATION: The proposed program will provide an alternative career path for students who have an initial interest in science or engineering but later decide against a course of study preparing for either a traditional STEM career or secondary school teaching. By combining City College’s science and education expertise with new experiential science learning opportunities, this program can meet the challenges of educating students for the new 21st century knowledge- based economy. Due to the diversity of City College’s student body, the program will help address the citywide need for diversifying the professional workforce of New York City’s cultural institutions and educational centers. The program will be cost-effective as it does not require additional faculty hires.

THE CITY COLLEGE OF NEW YORK OF THE CITY UNIVERSITY OF NEW YORK

A PROPOSAL TO ESTABLISH A PROGRAM IN

SCIENCE LEARNING & PUBLIC ENGAGEMENT

LEADING TO A

BACHELOR’S OF SCIENCE (BS)

EFFECTIVE FALL 2019

SPONSORED BY

THE DEPARTMENT OF SECONDARY EDUCATION OF THE SCHOOL OF EDUCATION

APPROVED BY

SECONDARY EDUCATION DEPARTMENT CURRICULUM COMMITTEE: MARCH 22, 2018 SECONDARY EDUCATION DEPARTMENT: MARCH 29, 2018 SCHOOL OF EDUCATION CURRICULUM COMMITTEE: APRIL 18, 2018 SCHOOL OF EDUCATION FACULTY: MAY 10, 2018

College Representative: Dr. Tony Liss, Interim Provost Contact Person: Associate Professor Yael Wyner, Secondary Education Telephone: 212) 650-5869 Fax: 2(12) 650-7530 Email: [email protected]

Provost’s Signature:______

Provost’s Name: ______

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Table of Contents

i. Executive Summary …………………………………...... 4 ii. Abstract …………………………………...... 6 1. Purposes and Goals ………………...... 7 2. Needs A. National Needs ...... 8 B. Regional and Local Needs ...... 9 C. Institutional Needs ...... 11 3. Students A. Demand for a Bachelor’s Degree in Science Learning and Public Engagement ………..13 B. Enrollment Projections ...... 13 C. Recruitment & Retention Strategy ...... 14 D. Admission Requirements ...... 14 4. Curriculum A. Proposed Curriculum ...... 14 B. Sample Sequence of Courses ...... 18 5. Cost Assessment A. Faculty...... 19 B. Facilities and Equipment ...... 20 C. Library and Instructional Materials ...... 20 6. Evaluation A. Internal Evaluation...... 20 B. External Evaluation ...... 21 Appendix A: A. Sample Undergraduate Program Schedule (SED Form) …...... ….22 B. Course Descriptions for Required Courses …...... 23 C. List of New Courses Including Prerequisites ...... 29 D. Syllabi for New Courses ………………...... 30 Appendix B: Faculty Teaching Assignments (SED Form) …………………….……………………………………83

Appendix C: Budget Tables

A. Five Year Program Expenditures (CUNY Table)….……………………………….…………………..84 B. Five Year Projected Revenue Table (CUNY)………………………….…………………………………85 C. Five Year Supporting Material Expenditures Table/New Resources (CUNY) ……….….86 D. Supporting Materials for Projected Revenue Table (CUNY)...... 88 Appendix D: Letters of Support: Internal ...... 90

Appendix E. Letters of Support: External ...... 97 Appendix F: Articulation Agreement with Bronx Community College ……………………………………104 Appendix G: Sample Job Announcements .……………………………………………………………………………104

Appendix H: References ...... 150

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Executive Summary

To prepare students for 21st century knowledge-based careers, the City College of the City University of New York proposes to establish a Bachelor’s program in Science Learning and Public Engagement. This program aims to equip graduates to deliver STEM learning at a variety of organizations including cultural institutions, botanical gardens, museums, zoos, environmental education centers, community-based organizations, educational, science, health, and environmental non-profits and foundations, government, universities and for-profit businesses and media ventures.

This new program will be the only program in New York City specifically designed to prepare undergraduates for careers at informal science learning environments. An expanding field, according to the National Research Council of the National Academy of Sciences (2009) and the U.S. Department of Labor (BLSL, 2010), this new program can open up new career opportunities to City College graduates. Furthermore, museums are actively seeking to fill open positions with job candidates that match the profile of a typical City College student. They are seeking first generation college graduates from low- income urban communities to achieve their twin goals of maintaining their cultural relevance and improving minority representation (Ivey, 2015). New students, whether incoming freshmen or transfer students within CUNY, will choose to enroll in City College to participate in this exciting new major.

In addition, the Science Learning and Public Engagement program can improve City College’s six-year graduation rate by providing an alternative major opportunity for a subset of students who had planned to major in science or engineering, but then decide not to pursue the qualifications required for traditional careers in these professions. Annually, almost 50% of enrolled City College students plan to major in science or engineering. Two years later, at least a quarter of these students are not science or engineering majors. Of the students who no longer seek to pursue science or engineering degrees, half drop out of City College all together (based on 2014-2016 data). According to Division of Science and Engineering faculty, many of these students would be excited about a career which would allow them to share their passion for science in informal learning environments.

The proposed 51-58 credit major, determined in consultation with New York’s major science cultural institutions, is designed to develop expertise in science engagement in non-formal environments, curricular and instructional design, communications media, and non-profit program management. A sequence of science or engineering courses will allow students to choose a disciplinary area of concentration. In addition, majors will develop broad based science expertise through inquiry focused life, physical, and environmental science courses and through a course that explores scientific practice across the disciplines of science.

The new program will also require students to participate in field experiences that will develop their potential to engage a diverse audience of science learners. The centerpiece experiences are two

180

internships, which will bookend a student’s participation in this program. Many of New York City’s established cultural institutions offer paid internships geared towards qualified candidates from the underrepresented groups that make up City College’s student body. The internships, other fieldwork opportunities and student coursework will together increase student readiness for science learning and engagement careers. The internships will be particularly important for building qualifications for full- time entry-level positions in this field.

To grow and develop the Science Learning and Public Engagement major to its full potential, we will convene an advisory panel once a semester. We already have commitments from key educational staff at the American Museum of Natural History, Brooklyn Botanic Garden, Bronx Zoo/Wildlife Conservation Society, New York Botanical Garden, and the New York Hall of Science.

We project a cohort of 39 full and part time students to enroll in the program. The size of the program, means that students should not appreciably alter course enrollment numbers in existing courses and flexibility on course enrollment limits will allow absorption of additional students. New instructors will not be needed for required program courses that are already offered. Three full time faculty with enough capacity in their course load are available to teach these courses. As course demand increases in year 3 with increased program enrollment, an adjunct to teach one course per year is budgeted. Revenue projections are expected to increase as this new major aims to capture students who may not otherwise attend City College or who would otherwise leave City College without completing their degree.

As the flagship science campus in the CUNY system, City College is the ideal CUNY campus for a science learning and engagement program. This new Bachelor’s degree in Science Learning and Public Engagement can expand the career prospects of the many City College students interested in science. By combining City College’s science and education expertise with new experiential science learning opportunities, this program can meet the challenges of educating students for the new 21st century knowledge-based economy.

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Abstract

To prepare students for 21st century knowledge-based careers, the City College of the City University of New York proposes to establish a Bachelor’s program in Science Learning and Public Engagement leading to a Bachelor’s of Science (BS) degree. This program aims to equip graduates to deliver STEM learning at a variety of organizations including cultural institutions, botanical gardens, museums, zoos, environmental education centers, community-based organizations, educational, science, health, and environmental non-profits and foundations, government, universities and for-profit businesses and media ventures. This new program will be the only program in New York City specifically designed to prepare undergraduates for careers at informal learning environments. The 51-58 credit major is designed to develop expertise in science engagement in non-formal environments, curricular and instructional design, communications media, and non-profit program management. Science or engineering courses will allow students to choose a disciplinary area of concentration. In addition, majors will develop broad based science expertise through inquiry focused life, physical, and environmental science courses and through a course that explores scientific practice across the disciplines of science. Furthermore, the new program will require students to participate in field experiences that will develop their potential to engage a diverse audience of science learners. The centerpiece experiences will be two internships, which will bookend students’ participation in this program. We expect for this program to provide graduates with the knowledge and skills required for successful careers in science learning and public engagement.

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1. Purposes and Goals

City College of the City University of New York proposes to establish a Bachelor’s degree program in Science Learning and Public Engagement leading to a Bachelor’s of Science (BS) degree. To prepare students for 21st century knowledge-based careers, this program aims to equip graduates to deliver STEM learning at a variety of organizations including cultural institutions, botanical gardens, museums, zoos, environmental education centers, community-based organizations, educational, science, health, and environmental non-profits and foundations, government, universities and for-profit businesses and media ventures. By combining integrated science course work and new experiential learning opportunities, the new program will significantly increase science career prospects for City College graduates (see figure 1 below).

Figure 1: The upper circle represents integrated coursework and new experiential learning opportunities that will expose students to the multiple disciplines of science and educational pedagogy. The lower circle represents the new experiential field work and career opportunities that will be available to students through the proposed program.

The new program will be a unique opportunity for CUNY to bolster its course offerings to tap into the fast-growing field of environmental and scientific careers in non-formal learning environments. The Science Learning and Public Engagement program will follow the museum education and science communication models of other national undergraduate degree programs. It will draw upon City College’s existing faculty expertise in education, science and engineering to create the only undergraduate program of its kind in New York City.

The Science Learning and Public Engagement program advances City College’s mission of educating New York’s diverse citizens, while at the same time advancing the City’s cultural, social and economic vitality. Located in the home of some the world’s most prominent cultural institutions and non-profits, City College, CUNY’s flagship science campus, is uniquely positioned to strengthen and diversify the nation’s

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cultural institutions. City College graduates are particularly appealing job candidates because as members of low-income families, underrepresented groups, and immigrant populations, they are from the same backgrounds that are the focus of many of the programs offered by the City’s cultural institutions. 2. Needs

A. National Needs

City College’s new program in Science Learning and Public Engagement will provide graduates with the science, education and outreach expertise needed to succeed in the expanding area of informal science learning. As a recent report of the National Research Council of the National Academy of Science (2009) states, “Learning science in informal environments is a vast and expanding area of study and practice . . . [that] include(s) not only science centers and museums but also a much broader array of settings.”

The proposed program will be the only undergraduate program in New York City specifically designed to prepare candidates for careers at informal learning environments such as museums and other non- profits like educational and community-based organizations. Additional employment opportunities will also be available in government and for-profit businesses. A review of Bureau of Labor Statistics estimates shows that museums and historical sites employ greater than 160,000 people nationwide (BLS 2017a), with education-related museum jobs becoming increasingly prominent (BLS, 2010). Museums are part of a robust non-profit employment sector, which comprises 10.2% of all private sector employment in the United States and almost 18% of employment in New York State (BLS 2017b). A growing sector of the United States economy, non-profits expanded employment opportunities by 8.5% from 2007-2012, even as the for-profit sector lost 3% of its jobs during the same time period (BLS 2017b).

As the museum sector grows, cultural institutions have begun to recognize the need for their workforce to better represent all of America’s cultural and ethnic diversity. On a national level, people of color represent 28% of total museum staff with percentages falling if maintenance and security jobs are excluded (Levvit, 2015). In New York City, while 67% of people identify as people of color, only 38% of cultural institution staff share this identity, and the percentages are lower for senior and mid-level staff (Schonfeld & Sweeney, 2016). To improve minority representation and cultural relevance, museums are advocating for the recruitment of students with the same population profile as City College undergraduates, first generation college students from low-income urban communities (Ivey, 2015). The new City College program is a response to this unmet need to expand the science engagement pipeline to members of underrepresented groups.

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The value of cultural and ethnic diversity for learning is an important emphasis of the National Research Council report on science learning in informal environments (2009). It is grounded in the need to provide multiple modes of entry into science learning experiences that engage student’s prior knowledge across many different ways of knowing and experiencing the world (Parker and Krockover, 2013). As the National Research Council (2009. p. 296) states: “Members of cultural groups develop systematic knowledge of the natural world through participation in informal learning experiences and forms of exploration that are shaped by their cultural-historical backgrounds and the demands of particular environments and settings. Such knowledge and ways of approaching nature reflect a diversity of perspectives that should be recognized in designing science learning experiences.”

B. Regional and Local Needs

Bureau of Labor Statistics data on the number of curators nationwide are useful for comparing New York City’s network of cultural institutions with the cultural institutions in other American cities. Bureau statistics indicate that New York City has more than double the number of curatorial positions than the next highest city (Boston), triple the next three highest cities (Los Angeles, Chicago, Washington, D.C.) and at least five times more positions than the other cities that round out the top ten list (BLS, 2017c). As a global megacity with a vibrant population of over 8.5 million people, it is not surprising that New York City has an unparalleled network of cultural institutions, each with its own non-formal educational career opportunities. All of the major science educational centers in New York City, like the American Museum of Natural History, Brooklyn Botanic Garden, New York Botanical Garden, New York Hall of Science, and the Wildlife Conservation Society, boast large education departments with multiple positions for which graduates of City College’s Science Learning and Public Engagement undergraduate program would be uniquely qualified. These institutions advertise for candidates with science and education backgrounds and with experience working with youths. In addition, as highlighted in the National Research Council report (2009), the Diversity in New York City Department of Cultural Affairs Community report (Schonfeld & Sweeney, 2016) and in letters from cultural institution representatives (see letters of support), these organizations are actively seeking candidates to represent New York City’s diverse cultural and ethnic perspectives. The new undergraduate program will be the only college program in New York City to address this need for diversifying the educational workforce of New York City’s science cultural centers.

A sampling of advertised permanent and full-time Science Learning and Public Engagement jobs in the New York City area (n=121) shows that graduates will be well-qualified for careers in a wide range of disciplines as follows: 1. Botanical gardens, cultural Institutions, museums and zoos 2. Environmental education centers and programs 3. Community based organizations 4. Educational non-profits and foundations 5. Science, environment and health non-profits 6. Schools, governments, and universities 7. Hospitals 8. For-profit businesses.

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The varied and robust job possibilities at New York City’s flagship cultural institutions are displayed in positions advertised at the American Museum of Natural History, Brooklyn Botanic Garden, New York Botanical Garden, New York Hall of Science, and the Wildlife Conservation Society. For example, the American Museum of Natural History has advertised for science educator positions in physical science, planetary science, and in their Urban Advantage program. The museum also advertised for positions to engage with students and alumni and with families, teachers, and schools. The Brooklyn Botanic Garden advertised for multiple positions in their gardening education program and for personnel to work with a partner school. The New York Hall of Science advertised for positions in its robust program cultivating STEM learning and careers in youths from underrepresented groups and the Wildlife Conservation Society posted job listings for educational staff to work with diverse audiences in their Bronx, Brooklyn, and Manhattan zoos. Smaller area museums and cultural organizations also offer career opportunities for candidates.

The New York area is also home to a wide variety of environmental education centers and programs, many of which are focused on making New York City’s environment accessible to New York’s urban residents. Christodora, an environmental education program in New York City advertised for educators and outreach coordinators to work with New York City youth. Green Girls, a youth development program for low income middle school girls, advertised for someone to manage their environmental and technology programming. Other organizations like the New York Restoration Project and Solar One are recruiting candidates for similar environmental and community focused positions.

Community-based organizations from around New York City also seek job candidates with expertise in urban youth development and education. Many of these organizations require candidates with life science education experience to develop educational programming around health-related topics. BronxWorks, an organization that helps individuals and families improve their economic and social well-being advertised for a recreation coordinator and health educator. Boom!Health, another Bronx based health organization advertised for a health and advocacy educator. The YMCA advertised for educational positions at locations around New York City. Other smaller organizations concerned with family and youth well-being, like Brotherhood/Sister Sol, Sheltering Arms and Harlem Educational Activities Fund, advertised for candidates to work in youth development.

The Science Learning and Public Engagement undergraduate degree will open up careers at non-profits that aim to improve educational access and STEM learning in New York City and nationwide. The Parris Foundation runs a program to increase STEM learning for low income urban children underrepresented in science. WISE (Working in Support of Education) seeks candidates to do educational outreach to schools and the College Board requires instructional designers for physics.

Science, environment, and health non-profits seek candidates to run education and outreach programs about science, the environment, and health issues like Tourette’s and colitis. The New York Academy of Sciences runs science education programs in conjunction with schools and the World Science Festival runs

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an annual science festival at locations citywide. The Urban Green Council does environmental outreach on sustainability and Phipps seeks to improve education in their low-income neighborhood schools.

Graduates will also be qualified for support positions in schools or alternative learning settings. Candidates will be qualified for school curriculum coordinator, science curriculum assessments teams, or parent- liaison positions. They will also be prepared to develop science curriculum for the New York City Department of Education or to work in an alternative learning environment like the THINK Global School.

Program graduates will be well-suited for government, university, and hospital positions that focus on science and/or education and outreach. New York City agencies have advertised for positions that focus on instructional materials, carbon footprint outreach, and for an urban park ranger position to lead educational programming throughout New York City. CUNY advertised for a candidate to provide STEM academic support to community college students. Memorial Sloan Kettering advertised for a clinical research coordinator with Spanish fluency for their study on immigrant health & cancer disparities.

New York City hospitals seek outreach staff for educational programming to local communities. Many of these positions require candidates to be Spanish speakers. In fact, 17% of all surveyed job listings (n=121) requested Spanish fluency and almost a third of all listings specified a preference for candidates with experience working with culturally diverse audiences.

Program graduates will also be prepared for varied careers at for-profit businesses. Graduates’ science expertise will make them eligible for curriculum and associate positions at educational startups like 2U Inc and for-profit education centers like Launch Math and Science Centers and NYC Data Science Academy. Graduates will also be prepared for STEM focused positions at media and publishing companies like Scholastic, Discovery Education, and Macmillan Learning. In addition, small health care businesses require the life science and educational expertise for advertised positions as conference producers and clinical educators.

C. Institutional Needs:

The new program builds upon CUNY’s commitment to provide its students with the tools to take advantage of growth areas in the New York City economy. In January 2017, CUNY announced a strategic framework for social and economic mobility in the 21st century knowledge economy. While aimed at the CUNY student population, the new program would build upon similar degree programs at universities across the United States. These programs include bachelor’s degrees in museum studies with a focus on science offered by Johns Hopkins, Central Michigan University, and Walsh University in Ohio. In New York City, the area of the country with the greatest employment opportunities in non-formal science

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learning, this program is unique. However, Stevens Institute of Technology in New Jersey offers a science communication undergraduate degree, not focused on education.

Currently, City College does not offer a major for students interested in science engagement in non- formal settings. This new program can expand the base of students interested in attending City College to students who are interested in pursuing careers in science learning and public engagement.

Current college students graduate with science-focused Bachelor’s degrees that do not prepare them to teach and communicate science in a non-formal setting. The only undergraduate science education program City College offers is a minor in secondary science education that it is geared towards teaching in schools and must be selected by students immediately upon starting college due to the high credit requirements. The new program in Science Learning and Public Engagement will allow City College to recruit students who are interested in science engagement careers outside of school settings. It will also open up to City College transfer students with Pathways credits, new and exciting employment opportunities around New York City. This new program has the potential to provide new career opportunities for the many students that transfer to City College from Hostos Community College, Bronx Community College, and the Borough of Manhattan Community College. An articulation agreement with Bronx Community College is attached and we are in the process of developing additional articulation agreements with other CUNY colleges.

Furthermore, the Science Learning and Public Engagement program will provide an alternative major opportunity for a subset of students who had planned to major in science and engineering fields, but then decide not to pursue the qualifications required for traditional careers in these professions. Annually, almost 50% of enrolled City College students plan to major in science or engineering. Two years later, at least a quarter of these students are not science or engineering majors. Of the students who no longer seek to pursue science or engineering degrees, half drop out of City College all together (based on 2014-2016 data). This new major will appeal to students who are more interested in science as it relates to the learning and engagement of others. It also can improve CCNY’s six-year graduation rate, as many of the students who had planned to major in science or engineering as incoming freshmen drop out of City College before they graduate.

Mandatory embedded internship-based experiential learning will expand student qualifications to prepare them for full-time entry-level positions in this field upon graduation.

The expansion of this type of learning is a goal advanced in the 2015 New York State budget because it is valuable for exposing students to new settings, for deepening student learning, and for helping students be more reflective about their role in society. The new proposed program is well-positioned for increasing internship and field work opportunities, as CUNY develops a plan to make experiential learning available to all CUNY undergraduates and potentially make it a graduation requirement.

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Many of New York City’s established cultural institutions offer paid internships geared towards qualified candidates from the underrepresented groups that make up City College’s student body. In addition, other internship opportunities are available through many of the smaller community-based organizations in New York City and through established government programs. We have a preliminary agreement from key individuals at these organizations to continue to work with us as members of an advisory panel, including representatives from the American Museum of Natural History, the Brooklyn Botanic Garden, the New York Academy of Sciences, the New York Botanical Garden, the New York Hall of Science, and the Wildlife Conservation Society/Bronx Zoo (see attached letters of support).

3. Students

A. Demand for a Bachelor’s Degree in Science Learning and Public Engagement

Informal science education centers around New York City interact with students who would like to pursue careers that focus on public engagement with science. This new major will appeal to those students. In addition, Division of Science faculty in Biology, Chemistry, Earth Science, Physics and in the Grove School of Engineering regularly encounter students who are engaged in the learning of scientific phenomena, but who do not necessarily wish to pursue science research or classroom teaching careers. Nevertheless, according to these faculty, these students are still interested in pursuing the myriad of informal science education careers that would allow them to share their passion for science with others (see letters of support).

Furthermore, as CUNY’s flagship science and engineering campus, City College has more students who express an interest in science and engineering than the other CUNY campuses. As such, the City College campus is the ideal location for this new program with more potential students that could feed into this program than any other CUNY campus. As described above, 50% of incoming students come to City College intent on majoring in science or engineering, but only a subset of interested students graduate with these majors. This new program in Science Learning and Public Engagement can meet the unmet student demand for a science focused major that would allow them to share their passion for science with others.

B. Enrollment Projections

Students who have completed introductory biology, chemistry, earth science, physics and engineering program courses are the population pool for the projected enrollment. Another source of students are community college transfer students who have completed science courses as part of their course of study. As the program gets up and running, we expect to enroll students who choose to attend City

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College expressly due to this major. We project a low rate of student attrition, as students who enter the program have already committed multiple semesters to science learning. Attrition that occurs will most likely be after completion of the first experiential learning opportunity. An important strength of the internship opportunity is exposure to real world science education careers that can help students better assess their match to the field.

Year 1 Year 2 Year 3 Year 4 Year 5 New Cont. New Cont. New Cont. New Cont. New Cont. F-T 10 0 10 10 15 10 15 15 15 15 P-T 2 0 2 2 3 4 3 5 3 6 Sub-totals 12 0 12 12 18 14 18 20 18 21 Totals 12 24 32 38 39

C. Recruitment & Retention Strategy

The targeted student body for the City College Bachelor’s degree program in Science Learning and Public Engagement will focus on undergraduates enrolled in introductory science and engineering courses. Science Learning and Public Engagement program faculty will visit each of these introductory courses (BIO 10100, 10200, Biological Foundations I & 2; CHEM 10301, 10401, General Chemistry 1 & 2; EAS 10600 Earth Systems Science; PHYS 20700, 20800, General Physics 1 & 2; ENGR 10100 Engineering 101) to share details about the program and major. Program faculty will visit faculty meetings of the Division of Science and School of Engineering to share details and a written description of the program. They will also share written program descriptions with science program advisors and student support services staff.

To help student retention, students will be required to meet with program faculty before each internship course. These checkpoints will be used to evaluate student progress and to assess student needs. They will also be used to determine if students require special support for timely completion of the program.

D. Admission Requirements

• Completion of 12 credits of science or engineering courses • Overall GPA of 2.7 plus GPA in science or engineering of 2.7

4. Curriculum

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The proposed curriculum, determined in consultation with New York’s major science cultural institutions, is a new 51-58 credit undergraduate major to prepare students for a wide array of science education careers. These include work in cultural institutions, botanical gardens, museums, zoos, environmental education centers, community-based organizations, educational, science, health, and environmental non-profits and foundations, government, universities and for-profit businesses.

A. Proposed Curriculum

Program courses are designed to develop expertise in science engagement in non-formal environments, curricular and instructional design, communications media, and non-profit program management. In addition, majors will develop content area expertise through science/engineering concentration courses and broad based science expertise through inquiry focused life, physical, and environmental science courses, and through a course that explores scientific practice across the disciplines of science.

Furthermore, the new program will require students to participate in field experiences that will develop their potential to engage a diverse audience of science learners. Two internship courses will punctuate student’s participation in this program. The first internship will occur once students have completed baseline introductory science courses in the first part of a student’s course of study. The second internship will be completed towards the end of a student’s course of study. The internships, other fieldwork and student coursework together will increase student readiness and qualifications for science learning and engagement careers.

Required courses are the standard courses and subject specific science or engineering concentration courses listed below:

STANDARD COURSES

Course Number Course Title Credits

SCIE 33000 Science Engagement in Non-Formal Environments 3

SCIE 36000 Exploration of Non-Formal Learning Resources 1

SCIE 47000 Science Engagement Internship 1 1

SCIE 48000 Science Engagement Internship 2 2

SCIE 44000 Science Practice Across Disciplines 3

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SCI 12400 Principles of Physical Science 3

SCI 12500 Principles of Life Science 3

SCI 12600 Principles of Environmental Science 3

EDSE 45103 Curriculum and Instruction in Science Education 4

EDLS 42500 Developing Programs for Non-Profit Organizations 3

MCA 10500 Introduction to Media Production 3

Electives selected in consultation with program advisor. Approved 9 electives include but are not limited to EDCE 20600 Observing Children and Their Development; EDCE 31904 Science in Early Childhood Settings; EDSE 44300 Methods of Teaching Science; SPED 32000 Introduction to Inclusive Education; SCIE 42000 Science in the Urban Environment; SCIE 49000 Science Engagement Internship 3; Science courses at the 200 level and higher.

Total Number of Standard Credits for Major 38

A sequence of subject specific content courses will allow students to choose a disciplinary area of concentration.

Biology Concentration Course Sequence:

Course Number Course Title Credits

BIO 10100 Biological Foundations I 4

BIO 10200 Biological Foundations II 4

BIO 20600 Introduction to Genetics 4

One additional 3 or 4 credit biology elective course selected in consultation with 3-4 program advisor.

Subtotal Number of Credits (Biology option) 15-16

Chemistry Concentration Course Sequence:

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Course Number Course Title Credits

CHEM 10301 General Chemistry I 4

CHEM 10401 General Chemistry II 4

CHEM 24300 Quantitative Analysis 4

CHEM 26100 Organic Chemistry I 3

CHEM 40600 Environmental Chemistry 3

Subtotal Number of Credits (Chemistry option) 18

Earth and Atmospheric Science Concentration Course Sequence:

Course Number Course Title Credits

EAS 10600 Earth Systems Science 4

Choose one of the following courses:

EAS 10400 Perspectives of Global Warming 3

EAS 10100 The Atmosphere

Choose two of the following courses:

EAS 22700 Structural Geology 4 EAS 44800 Terrestrial Aquatic & Atmospheric Systems 4 EAS 33000 Geographic Information Systems 3 EAS 36500 Coast and Ocean Processes 3 Subtotal Number of Credits (Earth and Atmospheric Science option) 13-15

Physics Concentration Course Sequence:

Course Number Course Title Credits

MATH 20100 Calculus I 4

MATH 20200 Calculus II 4

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MATH 20300 Calculus III 4

PHYS 20700 General Physics 1 4

PHYS 20800 General Physics 2 4

One standard elective course must be a 3-credit physics course selected in consultation with program advisor.

Subtotal Number of Credits (Physics option) 20

Engineering Concentration Course Sequence:

Course Number Course Title Credits

ENGR 10100 Engineering Design 1 1

PHYS 20700 General Physics 1 4

MATH 20100 Calculus I 4

MATH 20200 Calculus II 4

Choose two of the following courses:

ENGR 10300 Computer-Aided Analysis Tools for Engineers 2 ENGR 10610 Introduction to Earth System Science & Engineering 4 ENGR 27600 Engineering Economics 3 CSC 10300 Introduction to Computing 3 Subtotal Number of Credits (Engineering option) 18-20

Environment Concentration Course Sequence:

Course Number Course Title Credits

BIO 10200 Biological Foundations II 4

Choose one of the following courses:

BIO 22800 Ecology and Evolution 4

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BIO 33000 Survey of the Vertebrates 3

BIO 34000 Biology of Invertebrates 4

BIO 34500 Botany 4

EAS 10600 Earth Systems Science 4

EAS 44800 Terrestrial Aquatic & Atmospheric Systems 4

Choose one of the following courses:

EAS 10400 Perspectives of Global Warming 3

EAS 10100 The Atmosphere

Subtotal Number of Credits (Environmental option) 18-19

Total Number of Standard Credits for Major 38

Total Number of Credits for Science or Engineering Concentrations 13-20

Total Number of Credits for entire Major 51-58

Pathways courses Fixed Common Core:

English Composition (6 credits)

Mathematical and Quantitative Reasoning (3 credits)

Life and Physical Sciences (3 credits satisfied in major: SCI 12400)

Flexible Common Core

World Cultures and Global Issues (3 credits)

U.S. Experience in Its Diversity (3 credits)

Creative Expression (3 credits)

Individual and Society (3 credits)

Scientific World (6 credits satisfied in major: SCI 12500 and 12600)

College Option

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Philosophy (3 credits)

Speech, (3 credits or exemption on the basis of demonstrated proficiency)

Foreign Language (6 credits in a single language, or exemption on the basis of proficiency)

Total Number of Additional Credits for entire Pathways 33

Free electives Total Number of Free electives to total 120 credits 29-36

B. Sample Sequences of Courses (see appendix A for more detail)

Fall 1 Spring 1 BIO 10100 (4) BIO 10200 (4)

PATHWAYS COURSE: FIQWS CE (6) PATHWAYS COURSE: ENGL 21003 (3)

PATHWAYS COURSE: MATH 195 (3) PATHWAYS COURSE: WCGI-HIST (3)

FREE ELECTIVE (3) PATHWAYS COURSE: CO (3)

FREE ELECTIVE (3)

Fall 2 Spring 2 FREE ELECTIVE (3) FREE ELECTIVE (3)

FREE ELECTIVE (3) FREE ELECTIVE (3)

BIO 20600 (4) BIO 22900 (4)

PATHWAYS COURSE: IS (3) PATHWAYS COURSE: US (3)

PATHWAYS COURSE: CO (3) PATHWAYS COURSE: CO (3)

Fall 3 Spring 3 EDCE 31904 (3) SCI 12600 (3)

SCIE 3300 (3) EDLS 42500 (3)

SCIE 3600 (1) SCIE 4700 (1)

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SCI 12500 (3) MCA 10500 (3)

PATHWAYS COURSE: CO (3) FREE ELECTIVE (3)

FREE ELECTIVE (3)

Fall 4 Spring 4 EDCE 31904 (3) SCIE 42000 (3)

SCIE 44000 (3) SCIE 47000(2)

SCI 12400 (3) EDSE 45103 (4)

FREE ELECTIVE (3) FREE ELECTIVE (3)

FREE ELECTIVE (3)

5. Cost Assessment

A. Faculty

The new program will not require additional faculty to teach program courses. The science and engineering content courses will not see increased enrollment due to this program, as these courses represent our source of recruited students. In addition, many of these courses are large lecture courses. All existing program courses are part of other established programs and the addition of program students should not appreciably alter these course enrollment numbers. Flexibility in course enrollment limits will allow absorption of additional students. New program courses will be staffed by three full- time faculty with enough flexibility in their course load to staff classes. Starting in year 3, an adjunct to teach one course per year will be required as course demand grows with increased program enrollment. See Appendix B for more detail on staffing qualifications in the Faculty Teaching Assignment (SED Form Faculty Information – Existing Core Faculty)

Core Existing Faculty Information

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Name Rank Yael Wyner Associate Professor Richard Steinberg Professor Hazel Carter Associate Professor

B. Facilities and Equipment

The School of Education already has classroom and laboratory facilities available for use by students in this program. No additional facilities or equipment will be required for this program.

C. Library and Instructional Materials

The library already supports access to a full suite of educational and scientific journals and books. No new library resources will be required. In addition, no new computer software, audio visual materials or subscription databases will be required to support this program.

In sum, no significant new costs are predicted for this program. In addition, revenue projections are expected to increase as this new major aims to capture students who would otherwise leave City College without completing their degree. See Appendix C for budget tables describing the costs and revenue associated with this program.

6. Evaluation

A. Internal Evaluation

We will include a variety of methods to evaluate program effectiveness and outcomes. The goal of this new program is for graduates to have learned the knowledge and skills required to be successful in science learning and public engagement careers. As such, we have designed a number of approaches to monitor and assess the quality of the program and career readiness of students.

1. Course surveys: We will supplement standard CCNY course surveys with specific questions about course utility for career preparation and course coherence with other major requirements. These course evaluations are important for measuring the effectiveness of course

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work for providing students with the necessary knowledge and skills for science learning and public engagement careers. 2. Surveys and interviews of course instructors: Instructor will provide feedback on student performance, the relationship between curricular content and the informal science education field, and recommendations for improvement. This evaluation method is important for optimizing course content for student learning career readiness. 3. Feedback from interns and internship providers: To maximize learning and to best meet the needs of host organizations, the internship course instructor will be in constant communication with every intern and supervisor. Supervisors will also complete final intern evaluations. This feedback structure provides a framework to improve course utility and coherence to better support City College students for successful informal science education careers. 4. Exit surveys for program completers: Students will evaluate the overall major and describe post-graduation career goals. They will provide suggestions for program improvement. These exit surveys are an opportunity to understand student perceptions of the utility of the program for learning. 5. Program checkpoints: Students will be required to meet with program faculty before each internship, once at the beginning and once at the end of the students’ participation in the program. Checkpoints will evaluate student progress and assess student needs to improve student retention and respond to needs. 6. Job placement tracking: Students enrolled in the Science Engagement in Non-Formal Environments course will be required to setup a profile with LinkedIn. Besides helping students network job opportunities, the LinkedIn accounts will help the program track career development for graduates. It will also allow for follow-up with students and will be used to support a strong City College program alumni network.

B. External Evaluation

In addition to the described internal evaluation plan, we have also received commitments from educational leadership of New York City’s major informal science learning centers to be a part of a program advisory panel. The panel will convene once each semester to discuss how to grow and develop the Science Learning and Public Engagement major to its full potential. Partner organizations with which we have commitments are the American Museum of Natural History, Brooklyn Botanic Garden, Bronx Zoo/Wildlife Conservation Society, New York Botanical Garden, and the New York Hall of Science (see letters of support-external).

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Appendix A: Table A: Sample Undergraduate Program Schedule

Term: Fall 1 Credits per classification Term: Spring 1 Credits per classification

Course Number & Title Cr LAS Maj New Prerequisite(s) Course Number & Title Cr LAS Maj New Prerequisite(s)

BIO 10100: Biological Foundations 1 4 4 4 BIO 10200: Biological Foundations 2 4 4 4

Pathways Course: FIQWS: CE 6 6 Pathways Course: ENGL 21003 3 3

Pathways Course: MATH 195 3 3 Pathways Course: WCGI-HIST 3 3

Free Elective 3 3 Pathways Course: CO 3 3

Free Elective 3 3

Term credit total: 16 16 4 Term credit total: 16 15 4

Term: Fall 2 Credits per classification Term: Spring 2 Credits per classification

Course Number & Title Cr LAS Maj New Prerequisite(s) Course Number & Title Cr LAS Maj New Prerequisite(s)

Free Elective 3 3 Free Elective 3 3

BIO 26000 Introduction to Genetics 4 4 4 BIO 10100, 10200 BIO 22900 4 4 4 BIO 26000

Pathways Course: IS 3 3 Pathways Course: US 3 3

Pathways Course: CO 3 3 Pathways Course: CO 3 3

Term credit total: 16 16 4 Term credit total: 16 16 4

Term: Fall 3 Credits per classification Term: Spring 3 Credits per classification

Course Number & Title Cr LAS Maj New Prerequisite(s) Course Number & Title Cr LAS Maj New Prerequisite(s)

EDCE 31904 Observing Children & Develop 3 3 SCI 12600 Principles of Envir.Science 3 3 3 Fulfills Pathways Req.

SCIE 33000 Science Engagement Non- 3 3 X BIO 10200, CHEM EDLS 42500 Developing Programs for 3 3 X Formal Environments 10401, EAS 10600, Non-Profit Organizations PHYS 20800, OR ENGR 10100

SCIE 36000 Exploration of Non-Formal 1 1 X BIO 10200, CHEM SCIE 47000 Sci. Engagement 1 1 X SCIE 33000 & BIO 10200 Res. 10401, EAS 10600, Internship 1 OR CHEM 10401 OR EAS PHYS 20800, OR 10600 OR PHYS 20800 OR

ENGR 10100; SCIE ENGR 10100 OR CHEM 33000 Co-req 10301 OR PHYS 20700

SCI12500 Principles of Life Science 3 3 3 Fulfills Pathways MCA 10500 Intro.to Media Production 3 3 3 ENGL 11000 OR FIQWS

Pathways Course: CO 3 3 Free elective 3 3

Free Elective 3 3

Term credit total: 16 9 10 Term credit total: 13 9 10

Term: Fall 4 Credits per classification Term: Spring 4 Credits per classification

Course Number & Title Cr LAS Maj New Prerequisite(s) Course Number & Title Cr LAS Maj New Prerequisite(s)

EDCE 31904 Science in Early Childhood 3 3 SCIE 42000 Science in Urban Env. 3 3

SCIE 44000 Science Practice Across 3 3 X BIO 10200, CHEM SCIE 47000 Science Engagement 2 2 X SCIE 33000 & BIO 10200 Disciplines 10401, EAS 10600, Internship 2 OR CHEM 10401 OR EAS PHYS 20800, OR 10600 OR PHYS 20800 OR ENGR 10100 ENGR 10100 OR CHEM 10301 OR PHYS 20700

SCI 12400 Principles of Physical Science 3 3 3 Fulfills Pathways EDSE 45103 Curriculum & Instruction 4 4

Free Elective 3 3 Free Elective 3 3

Free Elective 3 3

Term credit total: 15 9 9 Term credit total: 12 3 9

Program Totals: Credits: 120 Liberal Arts & Sciences: 93 Major: 54 Elective & Other: 33

Cr:= credits LAS = Liberal Arts and Sciences Maj = major requirement New = new course Prerequisite(s) = list prerequisite(s) for the noted courses

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. Indicate academic calendar type: X Semester Quarter Trimester Other (describe): . Label each term in sequence, consistent with the institution’s academic calendar (e.g., Fall 1, Spring 1, Fall 2) . Use the table to show how a typical student may progress through the program; copy/expand the table as needed.

Courses Descriptions for Required Courses

Standard Courses

SCI 12400: Principles of Physical Science: 3 credits. Explores the basic scientific content, processes, and approaches with an emphasis on depth of understanding in the domain of physical science. Subject matter is drawn from properties of matter, heat and temperature, energy, optics, and force and motion. Class format is a combination of interactive discussions, hands on activities, and participation in extended scientific processes. This course is one of three similar courses along with Principles of Life Science and Principles of Environmental Science which could be taken in any order.

SCI 12500: Principles of Life Science: 3 credits. Explores core topics in the biological sciences with an emphasis on depth of understanding of the subject matter and an awareness of the skills and methods used in the life sciences. Subject matter is drawn from cell and molecular biology, evolution, and ecology. Class format is a combination of interactive discussions, hands on activities, and participation in extended scientific processes. This course is one of three similar courses along with Principles of Physical Science and Principles of Environmental Science which could be taken in any order.

SCI 12600: Principles of Environmental Science: 3 credits. This undergraduate course explores core topics in the environmental sciences with an emphasis on depth of understanding of the subject matter and an awareness of the skills and methods used in the environmental sciences to better understand the interrelationships of the natural world. Subject matter is drawn from a variety of disciplines, including biology, chemistry, and earth science, and focuses on analyzing environmental problems both natural and human-made, and proposing alternative solutions to these problems. Class format is a combination of interactive discussions, hands-on activities, and participation in extended field studies. This course is one of three similar courses along with Principles of Physical Science and Principles of Life Science which could be taken in any order. This course satisfies the Physical Science requirement or the third science requirement for Childhood Education.

SCIE 33000: Science Engagement in Non-Formal Environments: 3 credits. This course focuses on science, science learning, and science engagement for all audiences in out-of-school settings such as museums, botanical gardens, zoos, parks, after-school community organizations, and camps. Students

will gain experience in evaluating scientific research and findings in the context of exhibits, after-school activities, social media, and science writing. Pre-requisites: BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100

SCIE 36000: Exploration of Non-Formal Learning Resources: 1 credit. This course is an opportunity to visit and observe non-formal science learning institutions such as museums, zoos, botanical gardens, and community-based organizations. Pre-requisites: BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100; Co-requisite SCIE 33000

SCIE 44000: Science Practice Across Disciplines 3 credits. This course explores the nature of science in varied scientific disciplines. It begins with an overview of the philosophy of science and then uses case studies from the different domains of science to investigate the role of disciplinary context in science. Each class will include a component bringing the nature of science and scientific thinking to the learning of science. Pre-requisites: BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20700 OR ENGR 10100

SCIE 47000: Science Engagement Internship 1: 1 credit. This course is a chance to participate in a science learning and engagement opportunity in a non-formal setting. Sponsor organizations will have an onsite supervisor that gives assignments, evaluates work, and ensures that internship is a valuable educational experience. Permission from instructor required. Pre-requisites: SCIE 33000 and BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100 OR CHEM 10301 OR PHYS 20700

SCIE 48000: Science Engagement Internship 2: 2credits. This course is a chance to participate in a science learning and engagement opportunity in a non-formal setting. Sponsor organizations will have an onsite supervisor that gives assignments, evaluates work, and ensures that internship is a valuable educational experience. Permission from instructor required. Pre-requisites: SCIE 33000 and BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100 OR CHEM 10301 OR PHYS 20700

SCIE 49000: Science Engagement Internship 3: 3 credits. This course is a chance to participate in a science learning and engagement opportunity in a non-formal setting. Sponsor organizations will have an onsite supervisor that gives assignments, evaluates work, and ensures that internship is a valuable educational experience. Permission from instructor required. Pre-requisites: SCIE 33000 and BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100 OR CHEM 10301 OR PHYS 20700

EDLS 42500: Developing Programs for Non-Profit Organizations: 3 credits. This course supports students in designing a nonprofit program and services that are integrated with the organization's mission, strategic planning, goals and needs of clients. The subsequent program plan also serves as a foundation from which to write program proposals to funders, conduct program evaluations and budgets for program development. Includes 5 hours of fieldwork.

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EDSE 45103: Curriculum and Instruction in Science Education: 4 credits. The history, philosophy and role of science education in formal and informal settings; the evolution of the science curriculum; instructional planning and multiple research- validated instructional strategies for teaching within the full range of abilities; adapting the curriculum for students with special needs and second-language learning students; literacy development by native English speakers, as well as English-language learners; and the use of technology in the curriculum. Includes 30 hours of fieldwork in a variety of educational settings.

MCA10500: Introduction to Media Production: 3 credits. This course introduces the fundamental elements of video production and is the "gateway" into the B.F.A. program. Projects produced in this course are used to evaluate a student's candidacy into the program. Using digital video cameras, students learn basic organizational, writing, camera, and editing skills through short group and individual exercises and projects. Visual storytelling and narrative structure in fictional and non-fictional forms are emphasized.

Science & Engineering Concentration Courses

BIO 10100 Biological Foundations I: 4 credits. Introduction to biology, emphasizing primarily the cell and molecular levels of organization. Topics include characteristics of life, cellular organization and diversity, chemistry of life, bioenergetics, reproduction and early development, and major living groups. The course features in-depth study of selected topics that provide the foundation for upper level study. Students develop critical thinking and technical skills that are essential for mastering the content areas and for being successful in upper level courses. These include: vocabulary skills, critical thinking, collaborative learning, microscopy, collection and handling of scientific data, and elements of scientific investigation. Required for Biology Majors. Pre-requisites: or Coreq.: MATH 19000 or MATH 19500 or MATH 20100 or MATH 20500. Materials Fee: $10. Contact Hours: 3 lct., 3 lab., hr./wk.

BIO 10200 Biological Foundations II: 4 credits. Second semester of Introductory Biology, emphasizing organismic biology, evolution, and ecology. Topics include heredity, macro- and microevolution, structure and function of body systems, and ecology. The course features a survey of topics in lecture and in-depth study of selected topics in laboratories and workshops. Students develop critical thinking and technical skills that are essential for mastering the content areas and being successful in further study. These include: vocabulary skills, problem solving, collaborative learning, computer skills, experimental design, collection and analysis of scientific data, and preparing scientific reports. Required for Biology majors. Pre-requisites: A grade of C or better in BIO 10100 or an equivalent course or permission of the instructor. Materials Fee: $10. Contact Hours: 3 lct., 3 lab., hr./wk.

BIO 20600 Introduction to Genetics: 4 credits. A thorough introduction to the principles of genetics. Using a combined cell biological and Mendelian genetic approach, the course covers DNA organization, chromosome structure, genes and alleles, and transmission of genetic information in normal and

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genetically compromised organisms. Pre-requisites: BIO 10100 and BIO 10200 or equivalent. Contact Hours: 3 lct., 1 rec., hr./wk.

CHEM 10301 General Chemistry I: 4 credits. This is the first semester of a two-semester general chemistry course-sequence. An in-depth introduction to the fundamental laws and techniques of chemistry for majors in science and engineering. Topics include: measurement; stoichiometry; the gaseous state; thermochemistry; atomic structure and chemical bonding; redox reactions; solids, liquids and intermolecular forces. Materials fee: $30. Pre-requisites: Grade of C or better in MATH 19500 or placement by the department. Contact Hours: 3 lect., 2 lab, 2 workshop hr./wk.

CHEM 10401 General Chemistry II: 4 credits. This is the second semester of a two-semester general chemistry course-sequence. An in-depth introduction to the fundamental laws and techniques of chemistry for majors in science and engineering. Topics include: chemical kinetics; chemical equilibrium; acids and bases; free energy, entropy and the second law of thermodynamics; electrochemistry; advanced bonding concepts; metals and coordination chemistry; and nuclear chemistry. Materials fee: $30.Pre-requesites: Grade of C or higher in CHEM 10301 or placement by the department. Contact Hours: 3 lect., 3 lab, 1 workshop hr./wk.

CHEM 24300 Quantitative Analysis: 3 credits. Volumetric, spectrophotometric and electrometric analyses.

CHEM 26100 Organic Chemistry I: 3 credits. An introduction to the chemistry of carbon compounds, current interpretation of the reactions and properties of these compounds. Pre-requisites: Grade of C or higher in CHEM 10401 10301 or placement by the department. Contact Hours: 3 lect., 1 rec., hr./wk.

CHEM 40600 Environmental Chemistry: 3 credits. Chemical cycles, aquatic chemistry and microbial biochemistry, phase interactions, water pollution and treatment, atmospheric chemistry and pollution, geochemistry, soil chemistry, energy resources, hazardous wastes, toxicological chemistry, and analytical methods. Intended to broaden the students' understanding of chemical processes taking place in our environment. The relationship between atmospheric, soil and water chemistry will be underlined. This course draws upon general, analytical and organic chemistry experience. Pre-requisites 24300 and CHEM 26100. Contact Hours: 3 hr./wk.

EAS 10600 Earth Systems Science: 4 credits. A systematic global view of the features, processes, and underlying scientific concepts of the earth, atmosphere, and oceans, emphasizing environmental applications. Materials Fee $10. Contact Hours: 3 lect., 3 lab. hr./wk.

EAS 10400 Perspectives of Global Warming: 3 credits. Provides a concise and current view of the factors governing global warming and climate change and its implications for society as a whole. The use of climate models and data analysis build an understanding of the quantitative elements of the climate

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system and demonstrate how climate change is measured. Topics include: Earth's energy balance, measuring climate change, statistical significance of cycles, natural and anthropogenic sources of climate change, consequences of climate change, and modeling and predicting climate change. This course is recommended for non-EAS majors with an interest in learning the science behind the climate change debate. Contact Hours: 3 lect., 3 lab. hr./wk.

EAS 10100 The Atmosphere: 3 credits. An introduction to the processes and phenomena of our atmosphere for non-science majors. Topics include clouds, sky color, greenhouse effect, storms, climates and Ice Ages. Contact Hours: 3 lect. hr./wk.

EAS 22700 Structural Geology: 4 credits. Geometry of elementary earth structures, especially faults and fractures, their modes of origin, stress analyses, and models. The mechanics of naturally occurring structures and their relationship to human-made structures. Includes earthquake mechanics and development of geological maps. Pre-requisites EAS 10000, EAS 10600 or ENGR 10610. Contact Hours: 3 lect., 2 lab. hr./wk.

EAS 44800 Terrestrial Aquatic & Atmospheric Systems: 4 credits. Overview of critical Earth systems and their interrelationships; Lecture component places environmental issues in an ecological framework; Hands-on laboratory component introduces concepts and methods used in Earth system analysis with emphasizes in sustainable management of aquatic, terrestrial and atmospheric systems. Data set analysis tasks are assigned and student presentations are given throughout this class. Pre-requisites EAS 10600 or ENGR 10610. Contact Hours: 6 hr./wk.

EAS 33000 Geographic Information Systems: 3 credits. Introduction to Geographic Information Systems using ArcGIS. Analysis of spatial data based on location. Hands-on work with downloading databases from the Internet, modification of formats, editing, and data analyses. Visual representation of data will emphasize different data types (point, linear, and spatial) and use of various analytical tools (IDW, spline, nearest neighbor, quadrat analysis, and different pattern types, such as random, clustered uniform, bi-modal, etc). Environmental Applications are stressed in class and include: Earthquake Patterns and Risk Analysis, Vegetation Patterns and Changes over Time, Patterns of Sea Level Change due to Global Warming, remote sensing of fracture patterns, aerosol dispersal over time, pollution plumes in subsurface groundwater. Contact Hours: 3 lect. hr./wk.

EAS 36500 Coast and Ocean Processes: 3 credits. Principles governing the atmosphere-land- ocean-biosphere interactions in coastal environments. Topics include: coastal dynamics, bathymetric features, sea-level change, wave formation, physicochemical properties of the ocean; coastal biogeochemical processes; remote sensing observations (land-atmosphere- ocean); coastal urbanization; atmospheric pollution and impacts on coastal ecosystems; coastal acidification; eutrophication; coastal hazards; human impacts & management of coastal zones. Pre-requisites EAS 10600 or BIO 10200, or permission of instructor. Contact Hours: 3 lect. hr./wk.

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MATH 20100 Calculus I: 4 credits. Limits, continuity, derivatives, differentiation and its applications, differentials, definite and indefinite integrals. Pre-requisites: A grade of C or higher in MATH 19500 or placement by the Department. Credit will be given for only one of the following courses: MATH 20100 (part of sequence of MATH 20100, MATH 20200, MATH 20300) or MATH 20500. Contact Hours: 4 hr./wk.

MATH 20200 Calculus II: 4 credits. Techniques of integration, improper integrals, infinite sequences and series, parametric equations, vectors and the geometry of space, functions of several variables and partial differentiation. Prerequisite: A grade of C or higher in MATH 20100 or placement by the Department. After completion of MATH 20900, only 3 credits will be given for MATH 20200. (Part of sequence MATH 20100, MATH 20200, MATH 20300.) Contact Hours: 4 hr./wk.

MATH 20300 Calculus III: 4 credits. Vectors, infinite series, Taylor's theorem, solid analytic geometry, partial derivatives, multiple integrals with applications. Interpretations and calculations using Matlab software. Prerequisites: A grade of C or higher MATH 20200 or placement by the Department. Contact Hours: 4 lect., 1 lab hr./wk.

PHYS 20700 General Physics 1: 4 credits. Calculus based introductory physics course covering: vectors, kinematics, Newton's laws, equilibrium, gravitation, motion in a plane, work and energy, impulse and momentum, rotation and angular momentum, simple harmonic motion, fluids, heat, and thermodynamics. (Required for all students in the Physical Sciences, Engineering and Computer Science.) Pre-requisite or corequisite: MATH 20200. Materials Fee: $10. Contact Hours: 3 lect.; 2 rec.; 2 lab.

PHYS 20800 General Physics 2: 4 credits. Calculus based introductory physics course covering: waves and acoustics, electrostatics, magnetism and electromagnetism, direct and alternating current, geometrical and physical optics. (Required for all students in the Physical Sciences, Engineering and Computer Science.) Pre-requisites: PHYS 20700, MATH 20300. Materials Fee: $10.Contact Hours: 3 lect., 2 rec. hr./wk., 2 lab/wrkshp. hrs.

ENGR 10100: Engineering Design 1: 1 credit. An introduction to the major engineering disciplines and contemporary issues impacting engineering. One hour per week will be devoted to lectures related to the above issues by prominent faculty and outside speakers. Two laboratory hours per week will provide an introduction to engineering practice through hands-on investigations, computer applications, design projects and student presentations. The laboratory experience will consist of a single 14-week module or a combination of a 10-week module and a 4-week module in various engineering disciplines. Currently developed modules include a 14-week module in design and construction of an electrical device, four 10-week modules in structural design, robotic control, electronics and software development and two 4-week modules in software development and nanotechnology. All investigations and design projects are performed in groups and presented in oral and/or written form. Pre-requisites

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or Co-req.: MATH 19500 (min. C grade). Open only to transfer students who have not completed MATH 20200. Contact Hours: 1 lec. hr/wk., 2 lab hrs/wk. Notes: FIQWS 10026 satisfies any requirement for ENGR 10100, as well as for ENGL 11000.

ENGR 10300 Computer-Aided Analysis Tools for Engineers: 2 credits. An introduction to computer aided analysis techniques necessary for the study of electrical engineering and the design of electrical systems. Concepts introduced through short lectures are examined thoroughly during computer workstation-based workshops. Among the topics studied are: functions of real variables and their graphs, complex numbers and phasors, linear algebra, difference equations with applications to signal processing, and an introduction to system analysis. Pre-requisites MATH 20100 (min. C grade). Contact Hours: 3 hr./wk.

ENGR 10610 Introduction to Earth System Science and Engineering: 4 credits. The goal of this introductory course is to obtain an understanding of the entire Earth system on a global scale by studying its component parts (Atmosphere, Hydrosphere, Geosphere, and Biosphere); the interactions, linkages and dynamic equilibrium among these components on various time scales; and external forces on the system. This formulation is then applied to understanding the impact and interaction of anthropogenic factors, including modern engineering systems, on the environment (complex non- engineered systems). Examples will include topics such as global warming and sea level rise, etc. Select Laboratory Exercises: Minerals and Rocks, Simple Systems Computer Models, Mapping, Remote Sensing Data Handling and Visualization (IDL/ENVI). Contact Hours: 3 hr./wk. lecture, 3 hr./wk. lab

ENGR 10610: Engineering Economics: 3 credits. History of economic thought from the engineering point of view of modeling and control: Adam Smith to Keynes to Krugman and Thurow. Nature of the corporation. Balance sheet analysis. Time value of money: simple and compounded interest, annuities and loans, cash flow, profitability analysis and DCF rate of return. Cost estimation, cost benefit analysis. Risk analysis: forecasting, cash flow, simple probability theory, decision trees. Pre-requisites: MATH 20100 (min C grade). Contact Hours: 3 hr./wk.

CSC 10300 Introduction to Computing: 3 credits. Basics of procedural computer programming (primarily in C++). This includes an understanding of datatypes and variables, branching and looping constructs, pointers and recursion. Basic hardware components in a typical computer system. Also covered are elementary data structures, the standard template library, the basics of object oriented programming, and basics of security-conscious programming. Pre-requisites: MATH 19500 (min. C grade). Co-requisite: MATH 20100 (min. C grade). Contact Hours: 2 class, 2 rec. hr./wk.

BIO 22800 Ecology and Evolution: 4 credits. Introduction to the basic principles of ecology and evolutionary biology emphasizing quantitative approaches and hypothesis testing. Computer literacy is attained using spreadsheets and the Internet. Corequisites: BIO 20600, MATH 20900 or MATH 17300 or Coreq MATH 20200. Materials Fee: $10. Contact Hours: 2 lect., 4 lab.

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BIO 33000 Survey of the Vertebrates: 3 credits. Survey of the major features of the vertebrates, including brief modern classification of the major groups and summary review of their morphological features, evolutionary history, distribution, ecology, and social behavior. Specific additional characteristics such as mimicry, ectothermy, endothermy, cannibalism, migration, predation, defense and use of venom will be discussed. Special attention is given to conservation, destruction of the environment and human impact on vertebrate life. Contact Hours: 3 hr./wk.

BIO 34000 Biology of Invertebrates: 4 credits. The structure and function of various invertebrates selected to illustrate morphological, physiological and ecological adaptations. Pre-requisites BIO 10200. 2 lect., Contact Hours: 4 lab. hr./wk.

BIO 34500 Botany: 4 credits. Survey of the structure, physiology, diversity and ecology of photosynthetic plants and fungi. Pre-requisites BIO 10200 and CHEM 10301. Contact Hours: 2 lect., 4 lab. hr./wk.

List of New Courses Including Prerequisites

SCIE 33000: Science Engagement in Non-Formal Environments: 3 credits. Pre-requisites: BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100

SCIE 36000: Exploration of Non-Formal Learning Resources: 1 credit. Pre-requisites: BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100; Co-requisite SCIE 33000

SCIE 44000: Science Practice Across Disciplines 3 credits. Pre-requisites: BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20700 OR ENGR 10100

SCIE 47000: Science Engagement Internship 1: 1 credit. Pre-requisites: SCIE 33000 and BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100 OR CHEM 10301 OR PHYS 20700

SCIE 48000: Science Engagement Internship 2: 2credits. Pre-requisites: SCIE 33000 and BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100 OR CHEM 10301 OR PHYS 20700

SCIE 49000: Science Engagement Internship 3: 3 credits. Pre-requisites: SCIE 33000 and BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100 OR CHEM 10301 OR PHYS 20700

EDLS 42500: Developing Programs for Non-Profit Organizations: 3 credits.

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New Course Syllabi on Following Pages

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School of Education

CITY COLLEGE of NEW YORK

City University of New York

The School of Education prepares knowledgeable, reflective, caring teachers and leaders for diverse communities.

Part I - Syllabus Fall 2018

SCIE 33000: Science Engagement in Non-Formal Environments

Class Meetings:

Location:

Instructor:

Email:

Office Hours:

Office:

Telephone:

1. SCIE 33000: This course focuses on science, science learning, and science engagement for all audiences in out-of-school settings such as museums, botanical gardens, zoos, parks, after-school community organizations, and camps. Students will gain experience in evaluating scientific research and findings in the context of exhibits, after-school activities, social media, and science writing. 3 hrs./week. 3 credits.

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2. Prerequisites: BIO 10200 OR CHEM 10401 OR EAS 10600 OR PHYS 20800 OR ENGR 10100. To most effectively learn about non-formal science learning environments, students need to be familiar with a selection of basic core science topics taught in these courses.

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3. Co-requisites: None

4. Place of course in curriculum: Required

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5. Working themes of the Conceptual Framework of the School of Education and how they are addressed in the course:

• Educating for and about Diversity: Students learn about the diversity of scientific settings and how to promote inclusiveness and people’s diverse experiences into non-formal science settings. • Developing In-depth Knowledge about the World: This course seeks to give students an introduction to how learning of science in non-formal settings is useful for understanding the natural world. • Becoming Skillful, Reflective Practitioners: Students are asked to think about the many ways science is presented in different settings in order to be better able to reflect on how to bring the learning of science to various settings. • Nurturing Leadership for Learning: Through peer interactions, students determine constructive methods to approach and think about science. • Building Caring Communities: Students interact with peers to build understandings of science and to determine the best ways to think about science learning in non-formal environments.

6. Student outcomes expected upon completion of course

Upon successful completion of the course, students will:

• Participate in science activities • Discuss and review current issues in science through online posts and classroom discussions • Analyze how science is communicated in written articles • Analyze the presentation of science in non-formal science institutions • Design and create a science learning that utilizes a non-formal science learning environment in NYC (e.g. New York Botanical Garden, Bronx River, Hudson River, American Museum of Natural

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History, New York Hall of Science, Bronx Zoo, Central Park, Brooklyn Botanic Gardens, Prospect Park, Prospect Park Zoo, Central Park Zoo etc.) • Present science learning opportunities to classmates • Respond to readings

7. Instructional methods implemented in the course:

• Reflection through written and face-to-face discussion • Individual presentation • Individual student writing • Mini-lectures on selected topics including reflecting on readings, short podcasts and videos • Formative instructor feedback on written work • Visits to non-formal science institutions • Guest visits by science staff affiliated with non-formal science learning environments • Citizen Science activities ______

8. Suggested multiple methods of assessing students:

• Attendance and Participation 15% • Science Communication Analysis 15% • Non-Formal Science Institution Visit 20% • Design and Creation of a Science Learning Opportunity 25% • Responses to the readings (5) 20% • Creation of a LinkedIn Account 5%

9. Explanation of the assessment criteria:

Attendance, punctuality, participation:

1. Attendance, punctuality, and participation – 20%:

Attendance is required.

There are limited class sessions, so attendance and punctuality are very important. Class time will be devoted to important discussions regarding science across contexts. The expectation is that you will be present, on time, and prepared for every class. Those who are more than 10 minutes late to class will be counted as late. Three late marks will count as an absence. Consult the CCNY academic calendar when

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planning vacations or travel. You are allowed one absence or three late arrivals with no questions asked. Beyond that, I reserve the right to lower your semester grade, if appropriate.

Students are expected to come to class prepared. This entails reading all of the assigned materials before class and completing all of the activities included in the assigned materials. Active participation and engagement in class discussions and activities are expected at all times.

2. Science Communication Analysis – 15% Choose a popular article or blog post about recent scientific research and describe the scientific findings and how the scientists came to their scientific conclusions. Evaluate the article for accuracy, how well it communicated the findings and how scientists came to their scientific conclusions. Do you understand why the scientists made these conclusions? List three other questions that you have about the scientific study that were not addressed in the article. Do you think the article should have addressed these questions or do you think these questions are beyond the scope of a popular article or blog post?

3. Non-Formal Science Institution Visit – 20% New York City is rich with community resources designed to enhance science learning. Visit one exhibition at a New York City institution of science learning and report how the exhibit is designed to foster science learning. What are the goals of the exhibit? How do the exhibit designers achieve their goals? Choose three exhibit elements and describe:

1. The science story of each exhibit element 2. The communication approaches used (visual, tactile, auditory, written text, etc.) 3. Your evaluation of the effectiveness of the exhibit for: a. Engaging visitors b. Telling its science story: i. How clear is the science story told? ii. What scientific evidence is shared? iii. If applicable does the visitor experience the scientific phenomena? How does the experience help with science learning?

4. Design and Creation of a Science Learning Opportunity – 25% Design a science learning opportunity in an area that interests you. This activity must include an immersive experience in which participants learn by doing. Activity must foster asking questions, synthesis of evidence, and argumentation. You will present this activity to the class.

5. Responses to the readings (5) – 20%

Write a one-page response to five readings of your choice. Late responses will not be accepted.

6. Creation of a LinkedIn profile. Set up a profile describing your educational background, expertise and interests. ______

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10. Grading Scale:

A+ 97-100 B+ 87-89 C+ 77-79 D+ 67-69

A 93-96 B 83-86 C 73-76 D 63-66

A- 90-92 B- 80-82 C- 70-72 D- 60-62

F Below 60

11. Required Readings:

American Association for the Advancement of Science Center for Engagement with Science & Technology. Visualizing data. Available from: https://www.aaas.org/page/visualizing-data

Beck, L. (2002). Interpretation for the 21st Century: Fifteen Guiding Principles for Interpreting Nature and Culture, Ch. 1.

Bonney, R., Shirk, J. L., Phillips, T. B., Wiggins, A., Ballard, H. L., Miller-Rushing, A. J., & Parrish, J. K. (2014). Next steps for citizen science. Science, 343(6178), 1436-1437.

Cairney, P., & Kwiatkowski, R. (2017). How to communicate effectively with policymakers: combine insights from psychology and policy studies. Palgrave Communications, 3(1), 37.

Eberbach & Crowley. (2005). From living to virtual: Learning from museum objects. Curator, 48(3), 317- 338.

Engber, D. (2013). Gladwell is Goliath. Slate. Available from: http://www.slate.com/articles/health_and_science/books/2013/10/malcolm_gladwell_s_david _and_goliath_reviewed.html

Gladwell, M. (2009). How David beats Goliath by Malcom Gladwell. The New Yorker. Available from: https://www.newyorker.com/magazine/2009/05/11/how-david-beats-goliath

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Jackson, A. (2014). The Power of Using Infographics to Communicate Science: Nature. Available from: http://blogs.nature.com/ofschemesandmemes/2014/01/20/the-power-of-using-infographics- to-communicate-science

Kisiel, J. (2010). Exploring a school-aquarium collaboration: An intersection of communities of practice. Science Education, 94(1), 95-121.

Lauber, T. B., Tidball, K. G., Krasny, M., Freitas, N., Griswold, B., Ukeritis, B., & Word, C. (2012). Environmental education in urban systems: an exploration in research and practice.

Lee, N. M., VanDyke, M. S., & Cummins, R. G. (2018). A missed opportunity?: NOAA’s Use of social Media to communicate climate science. Environmental Communication, 12(2), 274-283.

Melber, L.M. (2007). Non-formal Learning and Field Trips: Engaging Students in Standards-Based Experiences Across the K-5 Curriculum

National Research Council. (2009). Learning Science in Informal Environment: People, places, and pursuits. Washington, DC: The National Academies Press. https://doi.org/10/17226/12190

National Research Council. (2010). Surrounded by Science: Learning science in informal environments. Washington, DC: The National Academies Press. https://doi.org/10/17226/12614

National Research Council. (2015). Identifying & supporting productive STEM programs in out-of school settings. Washington, DC: The National Academies Press. https://doi.org/10.17226/21740

Neill, M. S., & Lee, N.(2016). Roles in Social Media: How the Practice of Public Relations Is Evolving. The Public Relations Journal. 10(2).

Smith, L. (2014). Outdoor Learning: Education’s Next Revolution? Salon. Available from: https://www.salon.com/2014/02/16/outdoor_learning_educations_next_revolution/

Su, L. Y. F., Scheufele, D. A., Bell, L., Brossard, D., & Xenos, M. A. (2017). Information-Sharing and Community-Building: Exploring the Use of Twitter in Science Public Relations. Science Communication, 39(5), 569-597.

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Zimmer, C. (2013). A Note to Beginning Science Writers. National Geographic. Available from: http://phenomena.nationalgeographic.com/2013/06/24/a-note-to-beginning-science-writers/

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12. Blackboard: All students must use CUNY electronic blackboard. It is assumed that you will check blackboard for all assignments and announcements. Please go onto your CCNY and have your messages forwarded to the account that you check on a regular basis. I will expect you to have read any emails sent from blackboard.

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13. Notes:

• Policy on Academic Integrity: Under the CUNY Student Academic Integrity Policy, available from: http://www.cuny.edu/about/info/policies/academic-integrity.pdf “Academic Dishonesty is prohibited in The City University of New York and is punishable by penalties, including failing grades, suspension, and expulsion, as provided herein.” Violations of this policy fall into these areas that include but are

not limited to:

• Cheating • Obtaining Unfair Advantage • Falsifying of Records and Official Documents • Plagiarizing Here are more details on plagiarism from the CUNY academic integrity policy: Plagiarism is the act of presenting another person’s ideas, research or writings as your own.

The following are some examples of plagiarism, but by no means is it an exhaustive list:

• Copying another person’s actual words without the use of quotation marks and footnotes attributing the words to their source. • Presenting another person’s ideas or theories in your own words without acknowledging the source. • Using information that is not common knowledge without acknowledging the source. • Failing to acknowledge collaborators on homework and laboratory assignments.

Internet plagiarism includes submitting downloaded term papers or parts of term papers, paraphrasing or copying information from the internet without citing the source, and “cutting & pasting” from various sources without proper attribution.

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I welcome any questions you may have concerning academic integrity and will do my best to help you understand the standards of academic scholarship. I use CUNY guidelines to sanction any incidents of academic dishonesty in my courses. Any student who violates this policy will FAIL the course.

• Attendance: Students will only be allowed one absence throughout the semester. For every unexcused absence beginning with the second one, 2% will be deducted from the final grade. • All written work must be typed and double spaced using 12-point Times New Roman font. • Punctuality: Class will begin promptly at 4:50 p.m. Tardiness will have a negative effect on the final grade. • Cell phones and other similar devices must be turned off BEFORE class. • Students are expected to edit all of their work carefully. Grammar and vocabulary errors will have a negative effect on scores and grades. • If (and only if) the professor decides to accept a late assignment, s/he will automatically deduct 50% from the grade. • Course withdrawal period ends on XXXXX. ______

Part II – Tentative Course Outline

The syllabus is subject to change due to the needs of the class. If changes occur, students will be notified in a timely manner, either during class or through email. Therefore, all students must check their emails on a regular basis and are responsible for information distributed on and off line.

DATE Topics Readings

Week 1: Introduction

Week 2: Theoretical Perspectives of Non- NRC (2009) Chapter 2: Theoretical Formal Science Perspectives

Week 3: Science & Engagement Across Audiences Beck, L. (2002). Interpretation for the 21st Century: Fifteen Guiding Principles for

Interpreting Nature and Culture, Ch. 1.

The Power of Using Infographics to Communicate Science

AAAS Center for Engagement with Science & Technology

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How to communicate effectively with policymakers: combine insights from psychology and policy studies by Cairney & Kwiatkowski

Week 4: Science through Museum Exhibits (visit by NRC (2009) Chapter 5: Science Learning in curator from the American Museum of Designed Settings Eberbach & Crowley. Natural History) (2005). From living to virtual: Learning from museum objects. Curator, 48(3), 317-338.

Week 5: Science through Popular Science Writing NRC (2009) Chapter 8: Media and Media (visit by writer for Scientific A Note to Beginning Science Writers by Carl American) Zimmer Read (6/24/2013; National Geographic)

Gladwell is Goliath by Daniel Engber (10/7/2013)

How David beats Goliath by Malcom Gladwell (5/11/2009)

Science Communication Analysis Due Week 6: Science in Outdoor Settings (visit by Outdoor Learning: Education’s Next science educator at the New York Revolution?; Laura Smith 2/16/2014

Botanical Gardens) Environmental Education in Urban Systems: An exploration through research and practice, Laubar et al. (2012).

Non-Formal Science Institution Visit Due

Week 7: Online and Web Resources Produced American Museum of Natural History – By Non-formal Science Centers Science Bulletins

(http://www.amnh.org/explore/science-

bulletins)

Audubon Society (http://www.audubonadventures.org/)

Research 3 more websites from three other non-formal science institutions and be prepared to discuss the science presented in them with the class.

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Week 8: Non-formal Science Experiences (visit Melber, L.M. (2007). Non-formal Learning by educator at the American Museum and Field Trips: Engaging Students in

of Natural History) Standards-Based Experiences Across the K- 5 Curriculum

Kisiel, J. (2010). Exploring a school- aquarium collaboration: An intersection of communities of practice. Science Education, 94(1), 95-121.

Week 9: Science Community Based After- NRC (2009) Chapter 6: Programs for Young School Programs and Camps (visit by and Old

New York Academy of Sciences NRC (2015) Chapter 7: Culture, Diversity & mentor) Equity

“Where and how young people learning STEM” in Identifying & supporting productive STEM programs in out-of school settings

Week 10: Engaging the Public Through Citizen Citizen Science Opportunities: Science http://www.scientificamerican.com/citizen

-science/

Citizen Science: Next Steps for Citizen Science; Bonney et al. 2014, Science.

Week 11: Engaging the Public in Science Through A Missed Opportunity?: NOAA's Use of Blogging and Social Media Social Media to Communicate Climate

Science by Lee, Cummins & Van Dyke

Roles in Social Media: How the Practice of Public Relations is Evolving by Neill & Lee

Information-sharing and community- building: Exploring the use of Twitter in science public relations. Science Communication, by Su, Scheufele, Bell, Brossard & Xenos

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Setting up your LinkedIn account

Week 12: Assessment/Evaluation Experiential NRC (2015) Chapter 6: Assessing Learning learning, object learning Outcomes

Due: LinkedIn account setup

Week 13: In-class Lesson Presentations and Design and Create a Science Learning Reflections I Opportunity Due

Week 14: In-class Lesson Presentations II and Reflections on Science Learning in Non-formal Contexts

Bibliography:

American Association for the Advancement of Science Center for Engagement with Science & Technology. Visualizing data. Available from: https://www.aaas.org/page/visualizing-data

Beck, L. (2002). Interpretation for the 21st Century: Fifteen Guiding Principles for Interpreting Nature and Culture, Ch. 1.

Bonney, R., Shirk, J. L., Phillips, T. B., Wiggins, A., Ballard, H. L., Miller-Rushing, A. J., & Parrish, J. K. (2014). Next steps for citizen science. Science, 343(6178), 1436-1437.

Cairney, P., & Kwiatkowski, R. (2017). How to communicate effectively with policymakers: combine insights from psychology and policy studies. Palgrave Communications, 3(1), 37.

Eberbach & Crowley. (2005). From living to virtual: Learning from museum objects. Curator, 48(3), 317- 338.

Engber, D. (2013). Gladwell is Goliath. Slate. Available from: http://www.slate.com/articles/health_and_science/books/2013/10/malcolm_gladwell_s_david _and_goliath_reviewed.html

Gladwell, M. (2009). How David beats Goliath by Malcom Gladwell. The New Yorker. Available from: https://www.newyorker.com/magazine/2009/05/11/how-david-beats-goliath

Jackson, A. (2014). The Power of Using Infographics to Communicate Science: Nature. Available from: http://blogs.nature.com/ofschemesandmemes/2014/01/20/the-power-of-using-infographics-

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