Master of Science in Mathematics
The Master of Science in Mathematics offers students a unique blend of pure and applied mathematics. Students learn and gain research experience in mathematics from both theoretical and practical points of view. The core subject matter includes pure mathematics, mathematical modelling, applied mathematics, statistics and data analytics.
To be admitted, a student must meet the general admission requirements of the University, and must have an honours degree in Mathematics or a closely related area that includes courses in advanced calculus, basic probability and statistics, linear algebra, and abstract algebra. A final year average of at least B+ is usually required, along with a cumulative average of at least B+ in all mathematics and statistics courses. Students with minor background deficiencies will be required to include specific undergraduate courses in their programs. At the discretion of the Graduate Coordinator, up to two such courses may be required in addition to normal requirements. Such students may require longer than normal to complete the program. A student whose background contains major deficiencies may be required to complete appropriate senior undergraduate courses in order to qualify for the master's program. Normally a minimum B+ standing is required in these courses.
The MSc in Mathematics program is normally completed in three consecutive terms, beginning in the Fall or the Winter terms. Full-time students must complete the program within two years of initial registration. A grade of at least B- is needed to count a course or the seminar for credit towards the Master's degree.
The MSc in Mathematics may be completed through full-time or part-time studies and is offered in three options: thesis option, project option and course-based option.
Students in the thesis option must complete MA680 - Seminar in Mathematical Modelling in Finance and Science, MA699 - Master's Thesis and three approved courses (1.5 credits). For the thesis option, at most 0.5 credits at the 500 level may be counted towards the program credit requirement.
Students in the project option must complete MA680 - Seminar in Mathematical Modelling in Finance and Science, MA695 - Major Project, and five approved courses (2.5 credits). For the project option, at most 1.5 credits at the 500 level may be counted towards the program credit requirement.
Students in the course-based option must complete MA693 - Graduate Seminar and eight approved courses (4.0 credits). For the course-based option, at most 2.0 credits at the 500 level may be counted towards the program credit requirement.
Students can take an optional field of concentration. A maximum of one field of concentation is permitted as part of the MSc in Mathematics degree. There are six approved fields:
- Analysis and Geometry
- Computational Finance
- Discrete Mathematics and Algebra
- Financial Mathematics and Risk Management
- Mathematical Modelling
- Statistics and Data Analytics.
To complete a field, students in the thesis option must complete a minimum of 1.5 credits (including all required courses) listed in the Field and MA699 - Master's Thesis must be in the student's field of concentration.
To complete a field, students in the project option must complete a minimum of 2.0 credits (including all required courses) listed in the Field and MA695 - Major Project must be in the student's field of concentration.
To complete a field, students in the course-based option must complete a minimum of 2.5 credits (including all required courses) listed in the Field.
Analysis and Geometry
Required courses: (1.0 credit)
MA618 - Differential Geometry
One of MA550 - Real Analysis, or MA650 - Measure and Integration
Additional Concentration courses:
MA504 - Introduction to Complex Analysis
MA506 - Partial Differential Equations I
*MA550 - Real Analysis
MA555 - Continuous and Discrete Transforms
MA606 - Partial Differential Equations II
MA619 - Variational and Geometric Methods in Applied Mathematics
MA622 - Advanced Linear Algebra
*MA650 - Measure and Integration
*indicates course can be counted as an elective course for this field if it is not used as a required course.
Computational Finance
Required courses (1.0 credit)
MA671 - Computational Methods in Finance
ST674 - Monte Carlo and Simulation Methods
Additional Concentration courses:
One of MA507 - Numerical Analysis or MA571 - Computational Methods for Data Analysis
MA506 - Partial Differential Equations I
MA555 - Continuous and Discrete Transforms
MA570 - Financial Mathematics in Discrete Time
MA572 - Introduction to Optimization
MA606 - Partial Differential Equations II
MA632 - Optimization
MA651 - Stochastic Analysis
MA670 - Financial Modelling and Derivative Pricing in Continuous Time
MA677 - Quantitative Financial Risk Management
ST562 - Regression Analysis
ST662 - Advanced Regression Analysis
ST663 - Computational Statistics
ST673 - Financial Data Analysis
ST690 - Stochastic Processes
ST691 - Survival Analysis
ST692 - Time Series Analysis
ST694 - Statistical Learning
Discrete Mathematics and Algebra
Required courses: (1.0 credit)
One of MA523 - Introduction to Groups and Rings or MA538 - Graph Theory
One of MA625 - Group Theory or MA675 - Ring, Field and Galois Theory
Additional Concentration courses:
MA504 - Introduction to Complex Analysis
MA517 - Number Theory
*MA523 - Introduction to Groups and Rings
*MA538 - Graph Theory
MA572 - Introduction to Optimization
MA622 - Advanced Linear Algebra
*MA625 - Group Theory
MA632 - Optimization
*MA675 - Ring, Field and Galois Theory
*indicates course can be counted as an elective course for this field if it is not used as a required course.
Financial Mathematics and Risk Management
Required courses: (1.0 credit)
Two of MA570 - Financial Mathematics in Discrete Time, MA670 - Financial Modelling and Derivative Pricing in Continuous Time or MA677 - Quantitative Financial Risk Management
Additional Concentration courses:
One of MA507 or MA571
MA506 - Partial Differential Equations I
MA555 - Continuous and Discrete Transforms
*MA570 - Financial Mathematics in Discrete Time
MA572 - Introduction to Optimization
MA606 - Partial Differential Equations II
MA632 - Optimization
MA635 - Game Theory
MA651 - Stochastic Analysis
*MA670 - Financial Modelling and Derivative Pricing in Continuous Time
MA671 - Computational Methods in Finance
*MA677 - Quantitative Financial Risk Management
ST559 - Intermediate Probability Theory
ST562 - Regression Analysis
ST662 - Advanced Regression Analysis
ST663 - Computational Statistics
ST673 - Financial Data Analysis
ST674 - Monte Carlo and Simulation Methods
ST690 - Stochastic Processes
ST691 - Survival Analysis
ST692 - Time Series Analysis
ST694 - Statistical Learning
*indicates course can be counted as an elective course for this field if it is not used as a required course.
Mathematical Modelling
Required courses: (1.5 credits)
One of MA507 - Numerical Analysis or MA571 - Computational Methods for Data Analysis
MA606 - Partial Differential Equations II
MA680 - Seminar in Mathematical Modelling in Finance and Science
Additional Concentration courses:
MA505 - Ordinary Differential Equations
MA506 - Partial Differential Equations I
MA555 - Continuous and Discrete Transforms
MA572 - Introduction to Optimization
MA619 - Variational and Geometric Methods in Applied Mathematics
MA651 - Stochastic Analysis
MA660 - Dynamical Systems
MA664 - Mathematical Biology
ST674 - Monte Carlo and Simulation Methods
Statistics and Data Analytics
Required courses: (1.0 credits)
Two of ST662 - Advanced Regression Analysis, ST692 - Time Series Analysis, ST694 - Statistical Learning
Additional Concentration courses:
One of MA507 - Numerical Analysis or MA571 - Computational Methods for Data Analysis
MA555 - Continuous and Discrete Transforms
MA572 - Introduction to Optimization
MA632 - Optimization
MA651 - Stochastic Analysis
MA677 - Quantitative Financial Risk Management
ST544 - Introduction to Experimental Design and Survey Sampling
ST559 - Intermediate Probability Theory
ST561 - Theory of Statistics
ST562 - Regression Analysis
*ST662 - Advanced Regression Analysis
ST663 - Computational Statistics
ST673 - Financial Data Analysis
ST674 - Monte Carlo and Simulation Methods
ST690 - Stochastic Processes
ST691 - Survival Analysis
*ST692 - Time Series Analysis
*ST694 - Statistical Learning
*indicates course can be counted as an elective course for this field if it is not used as a required course.
Students should develop their program in consultation with their supervisor, Graduate Coordinator or designate.
