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CU Boulder.Pdf College of Arts and Sciences t 303 492 7294 Steven R. Leigh, Dean f 303 492 4944 Old Main 1-43 [email protected] 275 UCB Boulder, Colorado 80309-0275 TO: Professor Keith Julien, Chair, Department of Applied Mathematics FROM: Steven R. Leigh, Dean, College of Arts and Sciences SUBJECT: Proposed BA Degree in Statistics and Data Science DATE: 17 May 2017 On behalf of the College of Arts and Sciences, I strongly endorse the proposal to establish a BA Degree in Statistics and Data Science. The proposed degree helps further develop our capabilities in Science, Technology, Engineering, and Mathematics (STEM) fields, and will be a highly sought-after major in our College. The Department of Applied Mathematics will host this degree, and is well-positioned to do so. In addition, numerous other academic units will benefit from students who are trained in this program, and we can expect more formal relationships among our academic units in delivering this curriculum in the future. The core faculty identified for the degree are well-qualified to deliver the curriculum. In addition, the proposal includes faculty from other disciplines, indicating further possibilities for involving faculty from across our campus in training these students. The proposal does an excellent job in demonstrating ever-increasing demand for expertise in these areas. In particular, the Front Range will benefit considerably from students trained in these fields. More specifically, we have seen a major transformation in the last decade or so in our region, moving towards an economy that depends on high level skills in STEM disciplines. The proposed degree will enhance the ability of our campus to support the employment needs of many businesses in our area. It is also important to note that this degree will support a number of local companies that provide training in the areas of statistics and data science, as well as numerous software and data analytics enterprises in our region. Proposal for a Bachelor of Arts Degree in Statistics and Data Science Department of Applied Mathematics University of Colorado Boulder Spring 2016 updated Spring 2017 Contents 1 Description of Program 2 1.1 The Basic Design of the Program . .2 1.2 What's in a Name? . .4 1.3 Relation to other efforts on the CU Boulder campus . .6 1.4 Proposed student learning goals . .6 2 Concerns to be Addressed 10 2.1 Student Demand and Workforce Demand . 10 2.2 Role and mission criteria . 12 2.3 Duplication with programs at other Colorado institutions . 14 2.4 Statutory requirements . 15 3 Program Quality and Institutional Capacity 15 3.1 Admission, transfer, and graduation requirements . 15 3.2 Proposed program requirements . 16 3.3 Sample Curriculum . 18 3.4 Full list of APPM and STAT courses that support this program . 20 3.5 Assessment Plan . 22 3.6 Institutional Factors . 26 A Proposal Addendum 28 A.1 STAT course prefix . 28 A.2 Enrollment Projections . 29 A.3 Space requirements . 31 A.4 Projected new expenses and revenue estimates . 31 A.5 Teaching Faculty . 33 A.6 Related Consideration . 34 1 1 Description of Program 1.1 The Basic Design of the Program Faculty members of the Department of Applied Mathematics (APPM) in the College of Arts and Sciences at the University of Colorado Boulder propose the creation of a new degree: Bachelor of Arts in Statistics and Data Science. This degree will be the first of its kind in the University of Colorado system, and unique among undergrad- uate degrees offered in the state of Colorado. An undergraduate degree in statistics, with an emphasis on inter- and cross-disciplinary training, will prepare students for a wide range of careers, including, but not limited to, careers in engineering, eco- nomics, data science, public health, epidemiology, insurance, forestry, psychology, and social justice and human rights. Our increasingly data-rich and data-dependent world requires a workforce with the skills to create, interpret, and make rational de- cisions from data. This degree will increase the ability of the University of Colorado Boulder to attract high-quality resident and nonresident students; it will also put the Boulder campus in the position to place its alumni in highly desirable positions at top companies (e.g., Google, Apple, Amazon, Microsoft), national labs, and gradu- ate programs. \Data scientist" has been repeatedly voted as #1 position for college graduates, with excellent starting salaries. The faculty of APPM envision a truly interdisciplinary STEM (Science, Technol- ogy, Engineering, and Mathematics) degree program, where students will supplement core courses in applied mathematics and statistics with courses in the natural sci- ences, computation, engineering, social sciences, liberal arts, and/or in other areas that use statistics. The demand for such an undergraduate degree is high. The re- cently released 2014 statistics degree data from the National Center for Education Statistics show continued growth for bachelors degrees, with a 17% increase over 2013 numbers, and those 21% over the 2012 numbers. Overall, the number of bach- elors degrees in statistics has more than doubled since 2010 (see Figure 1). Masters degrees increased 45% and PhDs increased by 29% from 2010 to 2014. Additionally, many companies within Colorado are searching for statisticians and data scientists. A simple search in the CU Career Services database revealed 43 open positions, all in Colorado, during the first week of May 2017. To integrate the diversity of statistics interests across departments on campus, the statistics degree would be constructed to provide a strong statistical modeling and computing backbone, which would then support discipline-specific \specializa- tions". Students working toward a statistics degree would take several (18-24 credits) discipline-specific classes in other fields or departments, to obtain a concentration (or 2 Figure 1: Statistics degrees at the bachelors, masters, and doctoral levels in the United States These data include the following categories: statistics, general; math- ematical statistics and probability; mathematics and statistics; statistics, other; and biostatistics. Data source: NCES IPEDS. Figure Source: Taken from a October 2015 article for AmStat News by Steve Pierson, the American Statistical Association Di- rector of Science Policy. a full minor) in that field. This would also be a natural way to integrate statistical interests of other disciplines into the campus-wide statistics effort. This emphasis on an area of application is already part of the BS in Applied Math, so the faculty has experience working with these types of requirements. The fields in which a stu- dent could specialize include computer science (for a minor in \Computational Data Science"), information science (for a minor in "Human-Computer Interaction"), eco- nomics and finance, biomedical and life sciences, public health, physical sciences, social sciences, education, and engineering. The participating departments would work closely with Applied Mathematics/Statistics on recommending which of their discipline-specific statistics and modeling classes would count towards a minor. For a more complete list of suggested major courses see Sections 3.4 and 3.5. Nationwide, student demand for degrees in Statistics is expected to significantly outpace the enrollment growth for degrees in Physics and Mathematics, see Figure 2. Should this proposal be implemented, we expect demand for the new degree program will grow at a rate comparable to the national figures, roughly 15% per year, for at 3 least the first few years. However, the initial effort to establish it is expected to be relatively modest (relative to the costs of establishing a Department of Statistics). Almost all of the courses that are required by the major are already offered at CU Boulder, the majority by the Department of Applied Mathematics and some by other departments on campus. Further, numerous faculty members in the Department of Applied Mathematics have expertise in statistics (Jem Corcoran, Vanja Dukic, William Kleiber, Eric Vance, Brian Zaharatos), applied probability (Jem Corcoran, Anne Dougherty, Manuel Lladser), large data modeling and optimization (Stephen Becker, Ian Grooms, and Per-Gunnar Martinsson), and mathematical finance (Yu- Jui Huang). These faculty members can provide a strong base for teaching statistics courses currently offered in APPM, as well as provide meaningful undergraduate re- search opportunities. If the proposed Statistics and Data Science major is successful, we envision the need for 2 new tenure track faculty within the first 5 years of the pro- gram. The new faculty hires will be research active, have experience with large data and statistics, and will teach an expanded number of sections of statistics courses. They would also develop and teach the proposed Statistics Capstone course. 1.2 What's in a Name? Over the past twenty years, the era of big data has brought with it the need for people well-educated to analyze complex, high-dimensional data in order to develop good statistical models and make accurate predictions and reasonable decisions. To be successful in analyzing a wide variety of data, students ought to be exposed to a wide variety of statistical tools. High dimensional problems require analysts to approach problems in new and creative ways. Consequently, familiarity with traditional statistical methodology (e.g., data modeling, formal testing, regression diagnostics) along with cutting edge data analysis tools from computer science and machine learning (e.g., neural nets, decision trees, data management) is essential for preparing students to deal with complex, real-world problems. Our goal is help students gain access to a wide variety of tools and critical thinking skills so that they may solve a wide variety of problems. The current job market reflects the needs highlighted directly above. Statistics majors are often hired under a number of different job titles, including \data ana- lyst" and \data scientist". Often, data analyst and data scientist positions require knowledge of statistical theory and modeling and expertise in the handling of data, exploratory analyses, and hypotheses generation.
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