Applied Mathematics, MS

Eight graduate courses (32 semester hours of credit) are required for the degree: three required courses and five elective courses. The required courses provide a basic training in mathematical methods, and the elective courses include a wide variety of advanced topics. In addition, the program allows up to two of the elective courses to be taken outside the Department of Mathematics. No course can be used to satisfy both a requirement and an elective.

Complete all courses and requirements listed below unless otherwise indicated.

Core Requirements

Methods and Modeling
MATH 5131Introduction to Mathematical Methods and Modeling4
Algebra and Analysis
Complete one of the following:4
Analysis 1: Functions of One Variable
Algebra 1
Probability 1
MATH 7342Mathematical Statistics4
or MATH 7343 Applied Statistics


Complete one of the following two tracks:

Data Science Track

Data Science Courses
Choose two from the following:8
Machine Learning
Data Mining Techniques
Collecting, Storing, and Retrieving Data
Introduction to Data Mining/Machine Learning
Supervised Machine Learning and Learning Theory
Unsupervised Machine Learning and Data Mining
Introduction to Machine Learning and Pattern Recognition
Data Management and Database Design
Students may take other courses not on the list above from the College of Computer and Information Science in consultation with their faculty advisor.

Course Work Track

Course Work
Complete 8 semester hours. These courses may be chosen from outside the Department of Mathematics with faculty approval.8


Complete 12 semester hours in the following subject area:12

Program Credit/GPA Requirements

32 total semester hours required
Minimum 3.000 GPA required