Apr 23, 2024  
2022-2023 Graduate Catalog 
    
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2022-2023 Graduate Catalog [ARCHIVED CATALOG]

Data Science, M.S.

Coordinators: Ahmad Tafti, Weston D. Viles

Professors: Aboueissa, Bampton, El-Taha; Associate Professors: MacLeod, Suleiman; Assistant Professors: Eberly, Lanba, Sussman, Tafti, Viles, Ziller; Adjunct  Professor: Valentine 

The M.S. in Data Science is a multidisciplinary program and involves core computer science and statistics coursework with optional concentrations in application domains areas of interest. This degree program is intended for undergraduate and graduate degree holders with an interest in computing and data analytics as well as working professionals involved in analytics in industry. Prospective students from a diverse set of academic and vocational backgrounds are welcome to apply to this program.

Program Requirements

The 30-credit hour program includes a core set of required five courses (15 credits) that develop technical skills in the areas of computer science, mathematics, and statistics as well as three content area courses (9 credits) in a concentration track among one of business analytics, computation, geographic information systems (GIS), predictive analytics, prescriptive analytics, and public health.

Students must complete a capstone project that involves a practicum (3 credits) or a thesis (6 credits). Upon successful completion of the program, students will be able to:

  • collect, prepare, visualize, and analyze data,
  • interpret results in an interdisciplinary context,
  • use critical thinking skills and apply knowledge and methods when analyzing real world problems and developing state-of-the-art solutions,
  • communicate findings effectively to key stakeholders,
  • formulate and lead teams that can integrate the essential body of knowledge to produce solutions to real world problems,
  • understand and take into account ethical concerns associated with data collection, and
  • develop a strong sense of community identity, gaining perspectives by belonging and actively contributing to the scientific community. 

Required Courses (15 credits)

Students must complete the following five courses.

Concentration Track (9 credits)

Students must select from one of the following concentration tracks and complete three courses from within that track.

Business Analytics

  • BUA 601 Strategic Data Analysis
  • BUA 680 Foundations of Business Intelligence and Analytics
  • BUA 681 Data Management and Business Analytics
  • BUA 682 Data Pre-Processing for Business Analytics
  • BUA 683/MBA 677 Information Visualization
  • BUA 684 Business Data Mining and Knowledge Discovery
  • BUA 685 Problem Solving and Decision Analysis
  • BUA 686 Predictive and Business Forecasting
  • MBA 615 Ethical and Legal Issues in Business
  • MBA 623 Financial Engineering
  • MBA 629 Financial Modeling
  • MBA 669 Advanced Marketing Research

Computation

Cybersecurity

Geographic Information Systems

Predictive Analytics

Prescriptive Analytics

Public Health

Capstone (6 credits)

Students must complete either a thesis (DSC 698, 6 credits) under the supervision of their concentration track advisors, or a practicum/project (DSC 697, 3 credits) plus one additional data science course. 

Ethics

Students must complete ethics training prior to graduation.