MS Data Science & Statistics: Curriculum

Program Requirements for MS in Data Science and Statistics

To successfully complete the STEM-designated Master of Science in Data Science and Statistics program, students must earn 40 credits by taking 10 graduate-level courses. These include core foundational courses, electives in statistical analysis, machine learning, computational methods, or theory, and practical training courses. A minimum GPA of 3.0 is required, and students must receive a grade of B or higher in the capstone course, Math 540.

Core Courses (16 credits)

  • Probability and Statistics*: Math 501 (Probability) and Math 502 (Statistical Inference)
  • Computational Linear Algebra: Math 530
  • Statistical Modeling with Regression: Math 531

* Students may fulfill this requirement by taking Math 500 (Probability and Statistics for Data Science) instead of Math 501 and Math 502. In this case, one additional elective course must be completed.

Elective Courses (16 credits)

Complete four elective courses from a broad selection spanning the following areas:

  • Statistical Analysis
  • Data Science and Machine Learning
  • Computational Sciences
  • Advanced Theory

At least one course must be selected from the following: Math 532, 535, 543, 545, 546, 556, or 570. The remaining three electives may be chosen from a broader range of approved courses.

See below for a sample course catalog of electives.

Practical Training and Capstone (8 credits)

  • Practical Data Analysis: Math 534
  • Capstone Seminar: Math 540

Students enrolled in the Master of Arts in Statistics program must complete ten 4-credit courses and two 1-credit capstone seminars. Beginning in Fall 2025, students have the option to transition to the Master of Science in Data Science and Statistics program, pending approval by the graduate committee. Please consult with your advising faculty to plan your updated coursework accordingly.

Sample Course Catalog

Required Courses

  • Math 501: Probability
  • Math 502: Statistical Inference
  • Math 530: Computational Linear Algebra
  • Math 531: Statistical Modeling with Regression
  • Math 534: Practical Data Analysis
  • Math 540: Capstone Seminar

Elective Courses

Sample electives are grouped into four focus areas. Courses may vary by semester.

Statistical Analysis Focus

  • Math 532: Generalized Linear & Mixed Models
  • Math 556: Design of Experiments
  • Math 536: Nonparametric Smoothing and Semi-parametric Regression
  • Math 537: Reliability
  • Math 538: Sequential Analysis
  • Math 554: Sampling Theory
  • Math 557: Survival Analysis
  • Math 559: Time Series Analysis
  • Math 573: Applied Probability and Stochastic Processes
  • Topic Courses:
    • Causal Inference
    • Functional Data Analysis

Data Science and Machine Learning Focus

  • Math 535: Advanced Statistical Learning
  • Math 545: Principles of Data Science with R
  • Math 570: Data Mining with Multivariate Analysis
  • Topic Course:
    • Bayesian Statistics

Computational Sciences Focus

  • Math 543: Computational Statistics
  • Math 546: Scientific Computing with Python
  • Topic Courses:
    • Numerical Analysis

Theoretical Focus

  • Math 553: Nonparametric Inference
  • Math 555: Linear Models
  • Math 558: Multivariate Statistical Analysis
  • Math 571: Advanced Probability Theory
  • Math 572: Stochastic Processes
  • Math 579: Advanced Statistical Inference
  • Topic Courses:
    • Modern Nonparametric Methods
    • High-dimensional Probability
    • Theory of Machine Learning