MA in Math with Data Science Emphasis

Master of Arts in Mathematics with Data Science Emphasis

Candidates for the M.A. degree with Data Science emphasis must complete 30 hours of course work with at least 15 hours of courses numbered 5000 or above. The courses taken must include the data-science core courses and five elective courses chosen from the listed below. Up to 2 courses for data science electives can be substituted with other courses upon student's request and graduate program director's approval. All courses numbered below 5000 must be completed with grades of at least B.
Students who have already completed courses equivalent to the core courses may substitute other courses numbered above 4000. All substitutions of courses require the prior approval of the graduate director.
Thesis Option: The non-core course work may consist of an M.A. thesis written under the direction of a faculty member in the Department of Math & Statistics. A thesis is not, however, required for this degree. A student who wishes to write a thesis should enroll in 6 hours of MATH 6900. Students writing an M.A. thesis must defend their thesis in an oral exam administered by a committee of three department members which includes the thesis director.
Data Science Core:

MATH 4005 -- Exploratory Data Analysis with R

MATH 4200 -- Mathematical Statistics I

MATH 4210 -- Mathematical Statistics II

MATH 5070 -- Nonlinear Optimization

MATH 5250 -- Statistical Methods in Learning and Modeling

Data Science Elective Courses (choose 5 courses or choose 3 courses + 2 other courses approved by graduate director):

MATH 4220 -- Bayesian Statistical Methods

MATH 4260 -- Introduction to Stochastic Processes

MATH 5080 -- Scientific Computation

MATH 5090 -- High-dimensional Data Analysis

MATH 5225-- Statistical Computing

MATH 5320 -- Topics in Statistics and its Applications

MATH 5600 -- Topics in Computation

MATH 5770 -- Advanced Topics in Nonlinear Optimization

CMP SCI 5340 -- Machine Learning

CMP SCI 5342 -- Data Mining

For more information, please contact Dr. Qingtang Jiang at jiangq@umsl.edu.

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