Carnegie Mellon University


Since its founding, Carnegie Mellon University has emphasized cross-disciplinary research and education. In this innovative tradition, the MSCF program was formed in 1994 as the interdisciplinary collaboration of the Department of Statistics & Data Science in the Dietrich College of Humanities and Social Sciences, the Heinz College of Information Systems and Public Policy, the Department of Mathematical Sciences in the Mellon College of Science and the Tepper School of Business. The joint venture between these four colleges enables MSCF to offer a tight integration of statistics, computer science, mathematics and finance - the four disciplines underlying computational finance. 

Students learn traditional finance theories of equity and bond portfolio management, the stochastic calculus models on which derivative security trading is based and computational techniques such as Monte Carlo simulation, optimization, and the numerical solution of partial differential equations, using C++, Python R and Matlab programming. Students take a sequence of courses on modern data science, machine learning and time series analysis and apply these methods in courses on asset management, statistical arbitrage, risk management and market microstructure. Project work, some of which is team-based, is emphasized including a capstone course in financial engineering. A strong emphasis is placed on communication skills throughout the program.

We break our three-semester program into six, seven-week long “mini” semesters. Courses focus on the quantitative finance career paths of interest to our students: in tradingfinancial modelingquantitative portfolio management and risk management.

While students are permitted a degree of specialization in the last two mini-semesters through their choice of electives, the program is largely fixed with each set of courses preparing the groundwork for the next, more advanced, set. With all our students taking the same courses, recruiters value our students’ consistent ability to meet the analytical and technical challenges and opportunities facing the industry.