Master of Science in Applied Mathematics
The program prepares students for careers in science, engineering, and business, where advanced methods in differential equations, nonlinear optimization, statistics, and computational mathematics play a significant role in technology development and innovation. It accommodates individuals with varying academic backgrounds and career objectives, including students interested in pursuing a Ph.D. in the mathematical sciences. Upon completion of the program, students should have acquired significant knowledge and fundamental understanding across a broad range of subjects including analysis, differential equations, probability, nonlinear optimization, statistics, and numerical methods. To better prepare themselves for careers at the interface between mathematics and applications in science, engineering and business, our students are strongly encouraged to pursue deeper understanding in one of the three areas (program concentrations): Differential Equations, Optimization of Stochastic Systems, or Data Science.
Applied Mathematics Requirements
Common Core Courses
| MA 547 | Advanced Calculus I | 3 |
| | Or | |
| MA 635 | Functional Analysis I | 3 |
| | | |
| MA 540 | Introduction to Probability Theory | 3 |
| | Or | |
| MA 611 | Probability | 3 |
| | | |
| MA 615 | Numerical Analysis I | 3 |
Concentration in Differential Equations Elective Courses
Choose at least 3 courses from the following list for the concentration in Differential Equations:
| MA 649 | Intermediate Differential Equations | 3 |
| MA 650 | Intermediate Partial Differential Equations | 3 |
| MA 653 | Numerical Solutions of Partial Differential Equations | 3 |
| MA 681 | Complex Analysis with Applications | 3 |
Concentration in Optimization of Stochastic Systems Elective Courses
Choose at least 3 courses from the following list for the concentration in Optimization of Stochastic Systems:
Concentration in Data Science Elective Courses
Choose at least 3 courses from the following list for the concentration in Data Science:
| MA 544 | Numerical Linear Algebra for Big Data | 3 |
| | | |
| MA 541 | Statistical Methods | 3 |
| | Or | |
| MA 612 | Mathematical Statistics | 3 |
| | | |
| MA 641 | Time Series Analysis I | 3 |
| MA 661 | Dynamic Programming and Reinforcement Learning | 3 |
Electives
| MA 544 | Numerical Linear Algebra for Big Data | 3 |
| MA 541 | Statistical Methods | 3 |
| MA 612 | Mathematical Statistics | 3 |
| MA 613 | Spatial and Spatio-Temporal Statistical Modeling | 3 |
| MA 617 | Tensor Methods for Data Analysis | 3 |
| MA 620 | Pricing and Hedging | 3 |
| MA 623 | Stochastic Processes | 3 |
| MA 627 | Combinatorial Analysis | 3 |
| MA 629 | Nonlinear Optimization | 3 |
| MA 630 | Advanced Optimization Methods | 3 |
| MA 631 | Calculus of Variations | 3 |
| MA 632 | Theory of Games | 3 |
| MA 641 | Time Series Analysis I | 3 |
| MA 649 | Intermediate Differential Equations | 3 |
| MA 650 | Intermediate Partial Differential Equations | 3 |
| MA 651 | Topology I | 3 |
| MA 653 | Numerical Solutions of Partial Differential Equations | 3 |
| MA 655 | Optimal Control Theory | 3 |
| MA 661 | Dynamic Programming and Reinforcement Learning | 3 |
| MA 662 | Stochastic Programming | 3 |
| MA 681 | Complex Analysis with Applications | 3 |
| MA 711 | Inverse Problems in Science and Engineering | 3 |
| MA 712 | Mathematical Models of Risk | 3 |
| MA 720 | Advanced Statistics | 3 |
| MA 800 | Special Problems in Mathematics (MS) | 1-6 |
| MA 810 | Special Topics in Mathematics | 1-3 |
| MA 900 | Thesis in Mathematics | 1-10 |
Students may choose MA 900 - Thesis in Mathematics for six credits as one of their electives to work on a specific project with an advisor. Enrolling in MA 900 is subject to approval by the program coordinator.