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Requirements

The MS in Data Science & Statistics program requires ten 4-credit courses, including a 4-credit capstone seminar course, for a total of 40 credits. An exit exam or final thesis is not required. The ten courses include the followings:

• Probability and Statistics: Fulfilled by both Math 501 (Probability) and Math 502 (Statistics), or by Math 500 (Probability & Statistics for Data Science)
• Computational Linear Algebra: Math 530
• Statistical Modeling with Regression: Math 531
• Practical Data Analysis: Math 534
• At least one course from Math 532, 535, 543, 545, 546, 556, 570
• Capstone Seminar (4 credit): Math 540
• Elective courses from a collection of courses in statistics, data science, applied mathematics, and computational science.

The student must maintain at least a B average (GPA 3.0) and a minimum grade not lower than B– in these courses. In addition, the student must have a minimum grade not lower than B in the capstone seminar.

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.

Course Catalog

Core Courses
Course number	Title	Credits
Math 500	Probability & Statistics for Data Science	4
Math 501	Probability	4
Math 502	Statistical Inference	4
Math 530	Computational Linear Algebra	4
Math 531	Statistical Modeling with Regression	4
Math 534	Practical Data Analysis	4
Math 540	Capstone Seminar	4
Foundational Courses (select at least one)
Math 532	Generalized Linear & Mixed Models	4
Math 535	Advanced Statistical Learning	4
Math 543	Computational Statistics	4
Math 545	Principles of Data Science with R	4
Math 546	Scientific Computing with Python	4
Math 556	Design of Experiments	4
Math 570	Data Mining with Multivariate Analysis	4
Elective Courses
Math 536	Nonparametric Smoothing and Semiparametric Regression	4
Math 537	Reliability	4
Math 538	Sequential Analysis	4
Math 553	Nonparametric Inference	4
Math 554	Sampling Theory	4
Math 557	Survival Analysis	4
Math 559	Time Series Analysis	4
Math 573	Applied Probability and Stochastic Processes	4
Doctoral Level Courses (may be taken as electives)
Math 555	Linear Models	4
Math 558	Multivariate Statistical Analysis	4
Math 571	Advanced Probability Theory	4
Math 572	Stochastic Processes	4
Math 579	Advanced Statistical Inference	4