Computational Mathematics
In the era of big data and ever-increasing computer power, Computational Mathematics methods are key drivers of progress and innovation in many areas of application that include finance, data science (including machine learning), science and engineering, and numerous other areas of industry and government. Our interdisciplinary Master’s program is taught by professors from each of the five academic units in the Faculty of Mathematics.
Graduates will be able to effectively deploy a wide range of mathematical, statistical and computational techniques to solve large problems in science, industry, big data and commerce; to develop, enhance and maintain the relevant software tools; and to communicate results of complex methods and models to end-users.
Program
Master of Mathematics (MMath) in Computational Mathematics
- Twelve-month, intensive Master’s program in interdisciplinary computational mathematics.
- A fast track to PhD studies or to a high-end creative job in banking, technology, data science, manufacturing, biomedical applications, etc.
- This research-based program is fully funded with six courses in the first two terms and a supervised research project in the third term.
- Co-op option: Students can apply computational mathematics methods to real-life problems in the world of banking, machine learning, insurance, data science, manufacturing, or government. Co-op adds one term to the program duration.
Our program features core courses in the following areas:
- Discrete computational mathematics
- Numerical methods
- Computational statistics and machine learning
- Scientific computing
- Computational optimization
Admission requirements
For more information including admission and degree requirements, select your program of interest below.
