M.S. Mathematical and Computational Finance
The M.S in Mathematical and Computational Finance provides students with the theoretical knowledge as well as the practical methods and skills needed to begin or enhance careers as quantitative analysts in the financial industry. The program aims to provide the multidisciplinary foundations preparing quantitative analysts for this life-long development of skills and understanding.
We expect that, as a student and/or graduate of the M.S. in Mathematical and Computational Finance program, you will exhibit the following:
- Professionalism: Our students will develop professional collaboration, communication, and project management skills required for senior positions in industry, financial, investment, banking and insurance industries.
- Academic Excellence: Our students are dedicated toward building an academic foundation that bridges the critical intersection of mathematics, technology, and finance.
- Commitment to Growth and Development: Because of the evolving nature of financial markets and institutions, students of the field must be ready to learn new ideas and methods across a broad range of disciplines including mathematics, statistics, computational science, finance, and economics.
The M.S. in Mathematical and Computational Finance (MSMCF) provides students with the mathematical and computational tools, as well as the understanding of financial instruments and markets needed to obtain positions as quantitative analysts in financial institutions, including Wall Street investment firms. While students will take away a range of practical skills and theoretical knowledge from their study, the specific learning outcomes expected of graduates from the M.S. in Mathematical and Computational Finance program include:
- Ability to apply knowledge of mathematics and mathematical methods to the pricing and hedging of financial derivative securities.
- Ability to identify well-defined features of quantifiable systems.
- Ability to formulate a mathematical model of a quantifiable system.
- Ability to use mathematics to solve a mathematical model or problem. In particular, an ability to extract quantitative data and information from a mathematical model.
- Ability to distinguish between a good (or well-founded) mathematical model and a bad or (poorly-founded) model.
- Ability to communicate effectively. In particular, an ability to communicate concepts and methods of applied mathematics, and their relation to problems in other science and engineering disciplines.
- Ability to work effectively, both independently and as part of an interdisciplinary group.
- A recognition of the need for and an ability to engage in lifelong learning.