The MS in Applied and Computational Mathematics provides students with a rigorous, modern training in applied and computational mathematics and in the mathematics of data. The program is targeted to students with an undergraduate degree in mathematics or other quantitative disciplines such as computer science, statistics, economics and engineering. Through foundational and advanced coursework, students gain a strong combination of quantitative and computational skills as well as data fluency, positioning them for careers in industry or for advanced studies. Students can satisfy the 30-credit requirement in 12 to 24 months, with accelerated paths supported by relevant summer course offerings. Graduates are well-prepared for roles in information technology, finance, engineering, research, and education – particularly within the rapidly growing sectors of machine learning and artificial intelligence – or to pursue a PhD in the mathematical, statistical, and computational sciences.
Program Structure
The program has requirements for 30 graduate credits.
The core of 18 credits may be chosen from the three categories of (i) theory and modeling, (ii) computational methods and (iii) mathematical data science. The 12 electives may be chosen from a diverse set of mathematical topics including probability; real, complex and stochastic analysis; ordinary and partial differential equations; programming; optimization; machine learning; mathematical statistics; and high-performance computing.
Below is the bread down of the degree requirements:
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Required Courses
Students must complete 18 credits from the following Core categories. At least 6 credits must be completed from each category. At least two courses must be numbered 700 or higher.
| Theory and Modeling | 6 | |
| Applied Mathematical Analysis 2: Partial Differential Equations | ||
| Applied Dynamical Systems, Chaos and Modeling | ||
| Introduction to Stochastic Processes | ||
| Methods of Applied Mathematics 1 | ||
| Methods of Applied Mathematics-2 | ||
| Computational Methods | 6 | |
| Numerical Linear Algebra | ||
| Numerical Analysis | ||
| Methods of Computational Mathematics I | ||
| Methods of Computational Mathematics II | ||
| Stochastic Computational Methods | ||
| Mathematical Data Science | 6 | |
| Graphs and Networks in Data Science | ||
| Mathematical Methods in Data Science | ||
| Data-Driven Dynamical Systems, Stochastic Modeling and Prediction | ||
| Stochastic Computational Methods | ||
| Randomized Linear Algebra and Applications | ||
Electives
Students must complete at least 12 additional credits from the lists above or below. At most 6 credits can be taken from List B. At most one MATH course can be taken in coursework numbered 800-899.
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