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College of Professional Studies College of Professional Studies Katia Passerini, Ph.D., PMP 4. Completion of the following undergraduate Dean Division of Computer mathematics courses or equivalent: Alla Baeva, M.F.A. Science, Mathematics MTH 1008 - Matrix Methods Chair, Mass Communication MTH 1009 - Calculus I and Science: Master of MTH 1013 - Probability and Statistics I Antoinette Collarini-Schlossberg, Ph.D. MTH 1014 - Probability and Statistics II Chair, Criminal Justice, Science (M.S.) in Data Legal Studies & Homeland Security Keith Cozine Ph. D. Mining and Predictive Degree Plan Director, Doctoral Program in Homeland Security Analytics A. Required Courses (15 credits) Joan E. DeBello, Ed.D., DS 609 Advanced Managerial Statistics Chair, Division of Computer Science, Program Description 3 credits Mathematics and Science The M.S. program in Data Mining and DS 633 Applied Regression and Forecasting Emese Ivan, Ph.D., M.B.A. Predictive Analytics will combine the study Models 3 credits Chair, Division of Sport Management of data mining, predictive analytics, and CUS 510 Database System Design and Data Mark D. Juszczak, Ed.D. business intelligence. Predictive analytics Warehousing 3 credits Director, Graduate Program in International applies powerful statistical and data mining CUS 610 Data Mining and Predictive Communication techniques to large data sets in order to Modeling I 3 credits CUS 615 Data Mining and Predictive Christina Schweikert, Ph.D. generate useful information, identify patterns Modeling II 3 credits Director, Graduate Program in Data Mining and trends, and build models to predict future events. Applications of these techniques and Predictive Analytics B. Choice of Two Electives (6 credits) are now transforming decision-making throughout business, government, healthcare, CUS 640 Natural Language and Text Processing and academia. The demand for professionals CUS 625 Computer Visualization Applications knowledgeable in this area is projected to 3 credits Objective grow rapidly in the coming years. This M.S. CUS 630 Operations Research 3 credits degree is a STEM designated program. CUS 635 Web Data Mining 3 credits The Graduate Division of the College of CUS 670 Monte Carlo Techniques 3 credits Professional Studies is a uniquely structured CUS 675 Database Programming unit within the University offering academic Admission Requirements CUS 680 Distributed Big Data Analytics I degree programs in professional fields. The Admission to the program is contingent CUS 681 Distributed Big Data Analytics II College is committed to offering each student upon an assessment of the candidate’s an education that prepares that individual to ability to successfully pursue graduate study. C. Analytics Specialization (6 credits) make significant contributions to society, to This assessment will be made by examining C1. Marketing Analytics the local community and to his/her chosen previous academic performance, letters of MKT 508 Marketing Management profession. The mission is accomplished by recommendation, the applicant’s essay, work 3 credits MKT 611 providing an education which is value-oriented experience, performance on standardized Data Analysis in Marketing Research and consistent with the historical relationship of exams (such as the GRE), and any other 3 credits OR St. John’s University to the Catholic community. evidence that the admissions committee C2. Healthcare Analytics The uniqueness of the College comes from believes to be relevant. Either: MPH 204 Healthcare System and its blend of a strong liberal arts model of Applicants must submit the following its Financing education combined with a highly respected evidence of their ability to pursue graduate professionally oriented curriculum. Throughout study: Or: PAS 219 Healthcare Outcomes each of the college’s programs, an enriched 1. A baccalaureate degree from a regionally Assessment intellectual and academic environment is accredited college or university. Transcripts provided, enabling the student to explore and from each institution attended must be Followed by: HCI 520 Medical and Health develop an appreciation for truth and within submitted even if a degree was not conferred. Informatics which the value and dignity of the human 2. A record of scholarly achievement at the person is understood and respected. undergraduate level. Applicants are expected D. Capstone Course (3 credits) to have a 3.0 (based on a 4.0 scale) cumulative undergraduate grade point average, and a CUS 690 Applied Analytics Project 3 credits 3.0 in their major field of study. An applicant OR whose grade point average is below 3.0 may CUS 695 Software Implementation Project submit an official copy of his/her GRE to 3 credits support his or her application. Total: 30 credits 3. Two letters of recommendation from Completion Requirements individuals who can comment on the applicant’s academic abilities and potential All candidates admitted to the MS in Data to succeed in an academically rigorous Mining and Predictive Analytics degree program graduate program. At least one of these of study must complete all degree requirements letters must be from an instructor who has within five years of commencing study and taught and evaluated the applicant in an must complete the 30-credit program with a academic setting. minimum average of “B” (3.0 GPA). 184 Course Descriptions CUS 630 Operations Research CUS 681 Distributed Big Data Analytics II Pre/Co-requisite: CUS 610. Review of probability Prerequisite: CUS 680. An examination of the theory; stochastic processes; queueing theory; functional programming characteristics of CUS 510 Database System Design and Data inventory theory; review of solution of systems distributed algorithms. Building on Distributed Warehousing of linear equations; linear programming; Big Data Analytics I, we continue to explore the An examination of techniques used for duality; assignment and transportation ability to process and analyze massive datasets, database design, implementation, and problems; applications of mathematical models. but with particular attention to the algorithmic management. Design and construction of aspect. Students will be provided with the data warehouses, including choosing internal CUS 635 Web Data Mining necessary problem-solving and coding skills and external data sources, determining the Pre/Co-requisite: CUS 610. Investigation of required to tackle distributed big-data projects. degree of granularity, selecting time spans, and concepts and algorithms that add intelligence choosing how to group subjects. Introduction to web-based information systems in areas CUS 690 Applied Analytics Project to data mining, including definition, objectives, from business to healthcare to e-government Pre/Co-requisite: CUS 615. Data mining and query design and analysis of query results. to education. We will cover concepts from data analytics techniques will be applied in a domain mining and text mining as they apply to the area selected by each student. Knowledge CUS 610 Data Mining and Predictive web, and discuss the use of ontologies and discovery and predictive analytics have become Modeling I semantic web languages. valuable across data-rich disciplines and fields. Pre/Co-requisite: CUS 510. Serving as the Students will design and complete a project foundation of predictive analytics, this CUS 640 Natural Language and Text that involves collecting data and analyzing course focuses on identifying patterns and Processing information with the goal of generating useful relationships in data and the creation of models Prerequisite: CUS 620. The intent of this course knowledge. Domain applications may include to determine future behavior. Data mining is to present a fairly broad graduate-level areas such as: business and management, algorithms and techniques will be studied and introduction to Natural Language Processing finance and economics, medicine and applied to extract valuable information from (NLP, a.k.a. computational linguistics), which healthcare, public health, marketing and CRM, large data sets. The process of knowledge is the study of computing systems that can security, and social networks. discovery will be covered from data collection process, understand, or communicate in human and preparation to data analysis, model language. The primary focus of the course CUS 695 Software Implementation Project development, and deployment. Data mining will be on understanding various NLP tasks as Prerequisite: CUS 1126, or equivalent; COLLEGE OF PROFESSIONAL STUDIES algorithms for association, classification and listed on the course syllabus, algorithms for Pre/Co-requisite: CUS 615. Data mining, web prediction will be examined, along with the effectively solving these problems, and methods mining, and text mining methods will be development of models to predict categorical for evaluating their performance. There will be applied in the context of a software system. and continuous outcomes. also a focus on statistical learning algorithms Students will design and build a working that train on (annotated) text corpora to software implementation. Domain applications CUS 615 Data Mining and Predictive automatically acquire the knowledge needed to may be in areas such as business and Modeling II perform a task. management, finance and economics, medicine Prerequisite: CUS 610. As the second course and healthcare, social network mining, in the data mining and predictive modeling CUS 670 Monte Carlo Techniques e-government and education. sequence, this course includes topics such Prerequisites:
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