Masters in Data Science

This is a joint MS program offered by the departments of Computer Science (College of Engineering) and Statistics (College of Arts and Sciences). Half of the coursework for the degree consists of graduate courses in computer science while the other half consists of graduate courses in statistics. Students graduating will have the skills in computer science to handle large data sets (big data). They will be capable of writing software to work with these large data sets and they will further have the statistical skill to model and analyze data. They will possess sufficient expertise in both areas to effectively communicate with statisticians and computer scientists at the professional level.

The capstone for the program is a masters project course taken over two terms. In this course the student chooses a problem (topic) in Data Science on which to work under the supervision of a CS and/or STAT faculty member(s). At the end of the first term, the student will turn in a written proposal defining the problem, proposed solution(s), and a complete literature search. In the second term, the student will obtain a solution to the problem and present a written report defining the problem and his/her solution. Examples include: an unsolved problem involving data science at a local industry; an unsolved consulting problem involving data science drawn from a research problem at WMU; or an in depth study of a computationally intensive statistical method. Best projects, written in Sweave or Latex, will be submitted for publication in Data Science journals.

Admission requirements

  • A complete calculus sequence through multiple integration
  • A course in probability (post calculus)
  • A course in statistical methods
  • A course in linear algebra
  • Strong background in an object oriented programming language such as Java or C++


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Required and Elective Courses

Term one

  • STAT 6620: Applied Linear Models  (3 hours)

Term two

  • STAT Elective chosen from List 1 below (3 to 4 hours)
  • STAT 5860 - Computer Based Data Analysis (3 hours)
  • CS 5821 - Machine Learning (3 hours)

Term three

  • STAT 6800 - SAS Programming (3 hours)
  • CS 5430 - Database Systems (3 hours)
  • Part one of project

        STAT 6970 - Data Science Masters Project (2 hours)
        CS 6970 - Master's Project (2 to 6 hours)

Term four

  • STAT Elective chosen from List 1 below (3 to 4 hours)
  • CS Elective chosen from List 2 below (3 hours)
  • Part two of project

        STAT 6970 - Data Science Masters Project (2 hours)
        CS 6970 - Master's Project (2 to 6 hours)

Elective courses: List 1
Students who are considering a continuation onto a Ph.D. in Statistics (emphasis in Data Science) should take the sequence STAT 6500 and 6600.

  • STAT 5610: Applied Multivariate Statistical Methods  (3 hours)
  • STAT 5660: Nonparametric Statistical Methods  (3 hours)
  • STAT 5820: Time Series Analysis  (3 hours)
  • STAT 5850: Applied Data Mining  (3 hours)
  • STAT 6500: Statistical Theory I (4 hours)
  • STAT 6600: Statistical Theory II (4 hours)
  • STAT 6640: Design of Experiments (3 hours)

Elective courses: List 2

  • CS 5260 - Parallel Computations (3 hours)
  • CS 5300 - Artificial Neural Systems (3 hours)
  • CS 5560 - Network Programming (3 hours)
  • CS 6260 - Advanced Parallel Computations (3 hours)
  • CS 6530 - Data Mining (3 hours)