Masters in Data Science

This is an interdisciplinary master's program offered by the departments of Computer Science (CEAS) and Statistics (COAS). Half of the coursework for the degree consists of graduate courses in computer science while the other half consists of graduate courses in statistics. Graduates will be able to store and access data from a variety of sources, process Big Data architecture, apply analytic techniques and algorithms to large and complex data sets, apply data processing and visualization, work in collaborative teams, and communicate effectively.

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

  • Undergraduate program that includes calculus and linear algebra
  • A course in probability
  • A course in statistical methods
  • Strong background in an object oriented programming language such as Java or C++

Application

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Required and Elective Courses

Semester 1 (Fall)

  • STAT 6620 - Applied Linear Models  (3 hours)
  • STAT 5850 - Applied Data Mining  (3 hours)
  • CS 6100 - Advanced Storage, Retrieval and Processing of Big Data (3 hours)

Semester 2 (Spring)

  • STAT 5860 - Computer Based Data Analysis (3 hours)
  • CS 5610 - Advanced R Programming for Data Science (3 hours)
  • CS 5821 - Machine Learning (3 hours)

Semester 3 (Fall)

  • STAT 6800 - SAS Programming (3 hours)
  • CS 5430 - Database Systems (3 hours)
  • And MS Project 1

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

Semester 4 (Spring)

  • STAT Elective chosen from List 1 below (3 hours)
  • CS Elective chosen from List 2 below (3 hours)
  • And MS Project 2

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

List 1 - STAT Electives

  • STAT 5610: Applied Multivariate Statistical Methods  (3 hours)
  • STAT 5660: Nonparametric Statistical Methods  (3 hours)
  • STAT 5820: Time Series Analysis  (3 hours)
  • STAT 6500: Statistical Theory 1 (4 hours)
  • STAT 6600: Statistical Inference  (4 hours)
  • STAT 6640: Design of Experiments (3 hours)
  • STAT 6650: Advanced Statistical Inference (3 hours)

List 2 - CS Electives

  • 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)