Large streams of data have brought mathematical modeling to nearly every field and industry. More than ever, a deep understanding of the fundamentals and applications of these models is the differentiator between the success and failure of projects in statistics, machine learning, probabilistic modeling, and optimization. From constructing financial tools and optimizing supply chains, to computer-guided brain surgery and to quantum computing, a foundational understanding of advanced mathematics can give you the tools to create the ideas and technology that will drive the 21st century.

A graduate certificate in applied mathematics gives you the opportunity to study the fundamentals of statistical reasoning, mathematical modeling, and modern mathematical methods in a Tier 1 research department. Shorter than the full master's, the graduate certificate allows you to take up to four courses from the Department of Mathematics. Our courses cover a wide range of topics, from theory courses about the fundamental structures of mathematical objects, to project-based applied courses where students use modeling to solve research-level problems from academic and industry partners.

All applied mathematics courses are taught in the evening to accommodate working students. Mathematics and pure math courses also count toward this certificate.

Complete all courses and requirements listed below unless otherwise indicated.

## Core Requirements

Code | Title | Hours |
---|---|---|

Modeling | ||

Complete 4 semester hours from the following: | ||

Applied Linear Algebra and Matrix Analysis | ||

Introduction to Mathematical Methods and Modeling | ||

Numerical Analysis 1 | ||

Graph Theory | ||

Probability 1 | ||

Statistics | ||

Complete 4 semester hours from the following: | ||

Machine Learning and Statistical Learning Theory 1 | ||

Applied Statistics |

## Electives

Code | Title | Hours |
---|---|---|

Complete 8 semester hours from subject area MATH, including but not limited to the following: | ||

Analysis 1: Functions of One Variable | ||

Algebra 1 | ||

Topology 1 | ||

Partial Differential Equations 1 | ||

Numerical Analysis 2 | ||

Riemannian Optimization | ||

Optimization and Complexity | ||

Machine Learning and Statistical Learning Theory 2 | ||

Probability 2 | ||

Mathematical Statistics | ||

Applied Statistics | ||

Regression, ANOVA, and Design | ||

Stochastic Calculus and Introduction to No-Arbitrage Finance |

## Program Credit/GPA Requirements

16 total semester hours required

Minimum 3.000 GPA required