MS Mathematics: Financial Mathematics
Empowering students with advanced, practical mathematical and quantitative tools to succeed in high-value careers in modern finance
The Financial Mathematics emphasis within the Mathematics MS program prepares students for careers in the fast-growing and dynamic field of quantitative finance. This track features a multidisciplinary curriculum that integrates mathematics, statistics, machine learning, and coding, applied in the context of quantitative finance. A substantial portion of the coursework is offered in the evening, making the program especially supportive of students who are currently working.
The program can be completed on a full-time or part-time basis, and plans of study are flexible and can be tailored to individual academic and professional goals. Paths range from a focus on advanced applied mathematics with a quantitative finance perspective to a more technically intensive program aimed at high-impact quantitative roles in industry.
Plan type and coursework
The Master of Mathematics with an emphasis in Financial Mathematics is completed as a Plan C Master’s degree requiring 30 credits. The minimum GPA for the program is 3.0. Coursework used to fulfill degree requirements must be taken on a grade basis when offered. Students entering the program must have completed an undergraduate degree prior to matriculation.
Plans of study are flexible and may be tailored to individual goals, but all plans must be approved by the program.
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Financial Mathematics Foundations – 4 Courses
Financial Mathematics Foundations – 4 Courses (14 Credits)
Required core courses introducing financial mathematics and computational modeling:
- MATH 5075: Math Finance I
- MATH 5076: Math Finance II
- FM 5151: Financial Modeling I (Python)
- FM 5252: Financial Modeling II (C#)
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Core Coursework – 2 Courses
Core Coursework – 2 Courses (6 - 8 credits)
Select two courses from the following:
- Probability & Stochastic Processes
- MATH 5651 – Basic Theory of Probability and Statistics
- MATH 5652 – Introduction to Stochastic Processes
- Numerical Methods
- MATH 5485 - Introduction to Numerical Methods I
- MATH 5486 - Introduction to Numerical Methods II
- PDEs
- MATH 5587 - Elementary Partial Differential Equations I
- MATH 5588 - Elementary Partial Differential Equations II
- Data Science & Machine Learning
- MATH 5465 - Mathematics of Machine Learning and Data Analysis I
- MATH 5466 - Mathematics of Machine Learning and Data Analysis II
- Additional advanced options
- MATH 8401/8402 - Applied Mathematics Modeling
- MATH 8441 - Numerical Analysis and Scientific Computing
- MATH 8651 - Theory of Probability
- MATH 8654 - Probabilistic Modeling and Computation
- MATH 8442 - Numerical Differential Equations
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Financial Mathematics Elective – 2 Courses
Financial Mathematics Elective – 2 Courses
Select two courses from the following:
- FM 5323: Machine Learning in Finance
- FM 5343: Quantitative Risk Management
- FM 5411: Fixed Income Markets
- FM 5422: Quanitative Hedge Fund Strategies
- FM 5432: Portfolio Optimization
- FM 5462: Market Micristructure
- FM 5990: Topics in Financial Mathematics
These offerings are subject to changes. Please make sure to consult with your advisor.
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Outside Coursework – 2 Courses
Outside Coursework – 2 Courses (4 - 8 credits)
Select supporting courses from related fields such as computer science, statistics, or industrial engineering. Supportive coursework may also be chosen from the Core Mathematics list or Financial Mathematics electives, allowing additional flexibility in shaping an individualized program. Additional choices may be approved based on student interests and goals.
Examples include:
- Computer Science (CSCI)
- CSCI 5521 - Machine Learning Fundamentals
- CSCI 5523 - Introduction to Data Mining
- CSCI 5525 - Machine Learning: Analysis and Methods
- CSCI 5527 - Deep Learning: Models, Computation and Applications
- Statistics (STAT)
- STAT 5052 – Statistical and Machine Learning
- STAT 5101 - Theory of Statistics I
- STAT 5102 - Theory of Statistics II
- STAT 5302 - Applied Regression Analysis
- STAT 5401 - Applied Multivariate Methods
- STAT 5511 - Time Series Analysis
- STAT 5701 - Statistical Computing
- STAT 8056 - Advanced Machine Learning
- Industrial & Systems Engineering (IE)
- IE 5133 - Operations Research for Data Science
- IE 5441 - Financial Decision Making
- IE 5553 - Simulation
- IE 5571 - Reinforcement Learning
- IE 8521 - Optimization
- IE 8564 - Optimization for Machine Learning
Other courses in or outside of Mathematics may be used with advisor and DGS approval. Core or Elective courses in Mathematics as listed above may be used to fulfill the outside coursework requirement.
Tuition and funding
Offers of admission to our MS Mathematics: Financial Mathematics program do not come with an offer of funding. Students can find a number of financial aid opportunities through the Funding page of the Graduate School’s website.