Students pursuing the M.A. degree in mathematics may choose the traditional track of M.A. in either pure or applied mathematics or the track in data science.

The traditional track is well suited for students preparing to teach at the high school or junior college level. Those who concentrate on applied courses in the traditional track build a foundation for the application of mathematics in industry and the continuation of their education in the Ph.D. program in mathematical and computational sciences. The M.A. with data science emphasis is well suited for students preparing to work in industry as data scientists. Our graduates with data science emphasis will acquire a solid foundation in statistics and computational skills with emphasis on applications to data science. Students may enroll in any of these graduate programs on a part-time basis.


Applicants must meet the general graduate admission requirements of the Graduate School, described in the UMSL catalog. Students are considered for admission to the graduate program in Mathematics only after they have formally applied for admission through the Graduate School. Applications may be completed online. Additional Requirements are listed below.

Applicants must have at least a bachelor's degree in mathematics or in a field with significant mathematical content. Examples of such fields include computer science, data science, economics, engineering and physics. An applicant’s record should demonstrate superior achievement in undergraduate mathematics.

Students must also have completed mathematics courses equivalent to the following UMSL courses:

A student missing some of the above requirements may be admitted on restricted status if there is a strong supportive evidence in other areas. The student will have to take the missing courses, or demonstrate proficiency to the satisfaction of the Graduate Director. Special regulations of the Graduate School applying to students while they are on restricted status are described in the UMSL Bulletin.

Incoming students are assigned advisers with whom they should consult before each registration period to determine an appropriate course of study. If necessary, students may be required to complete undergraduate course work without receiving graduate credit.

Candidates for the M.A. degree must complete 30 hours of course work with at least 15 hours of courses numbered 5000 or above. All courses numbered below 5000 must be completed with grades of at least B. The selections of the courses numbered 5000 or above need the prior approval of the graduate advisor.

The courses taken must include the data-science core courses listed below and five elective courses chosen from the listed below in the data-science electives. Up to 2 courses in the data-science electives can be substituted with other courses upon student’s request and graduate program director’s approval.

Students who have already completed courses equivalent to those in the core may substitute other courses numbered above 4000. All substitutions of courses for those listed in the core require the prior approval of the graduate director.

Degree Requirements

Candidates for the M.A. degree may choose an emphasis in mathematics or data science. Students in the M.A. program who want to transfer to the Ph.D. program upon successful completion of 15 credit hours must fill out a new application through Graduate Admissions.

Students intending to enter the Ph.D. program must have a working ability in modern programming technologies. A student with a deficiency in this area may be required to take courses at the undergraduate level in computer science.

Applicants for the Ph.D. program must, in addition, submit three letters of recommendation.


Data Science Emphasis



Courses Required Hours
MATH 4005 Exploratory Data Analysis with R 3
MATH 4200 Mathematical Statistics I 3
MATH 4210 Mathematical Statistics II 3
MATH 5070 Nonlinear Optimization 3
MATH 5250 Statistical Methods in Learning and Modeling 3
Total Hours 15

Choose five of the following courses:

Courses
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

The non-core course work may consist of an M.A. thesis written under the direction of a faculty member in the Department of Mathematics and Computer Science. 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, M.A.Thesis. Students writing an M.A. thesis must defend their thesis in an oral exam oral exam administered by a committee of three department members which includes the thesis director.

Any student who intends to apply for financial assistance, in the form of a teaching assistantship or a research assistantship, is required to have three letters of recommendation submitted with the application to the graduate program in Mathematics. Applications for financial assistance should be submitted before February 15 prior to the academic year in which the student expects to begin graduate study. Notifications of awards are generally made March 15, and students awarded financial assistance are expected to return letters of acceptance by April 15.

For further information about our Graduate Degrees in Mathematics and Computer Science, financial aid, and the regulations of the Graduate School, see our page on advanced degrees.