Master of Mathematics (MMath) in Computational Mathematics - Co-operative Program

The program information below was valid for the fall 2023 term (September 1, 2023 - December 31, 2023). This is the archived version; the most up-to-date program information is available through the current Graduate Studies Academic Calendar.

The Graduate Studies Academic Calendar is updated 3 times per year, at the start of each academic term (January 1, May 1, September 1). Graduate Studies Academic Calendars from previous terms can be found in the archives.

Students are responsible for reviewing the general information and regulations section of the Graduate Studies Academic Calendar.

Program information

  • Admit term(s) 
    • Fall
  • Delivery mode 
    • On-campus
  • Length of program 
    • Full-time: 5 terms (20 months)
    • Part-time: students will be expected to complete the program in a time period of two to three years (Master's Research Paper option) to two to four years (Coursework option). The minimum duration of study for part-time students is two years. Students may not do the co-op option part-time. 
  • Program type 
    • Co-operative
    • Master's
    • Research
  • Registration option(s) 
    • Full-time
    • Part-time
  • Study option(s) 

Admission requirements

  • Minimum requirements 
    • An overall 78% average or its equivalent for undergraduate work.
    • A 4-year honours bachelor's degree or its equivalent with specialization in some area of the mathematical, statistical and computer sciences. Note: graduates of other quantitative and mathematically oriented programs are also encouraged to apply; this includes, but is not restricted to, graduates of commerce, economics, engineering, finance, and any of the physical sciences. The department graduate committee will determine the suitability of each student’s background for success in this program.
  • Application materials 
    • Résumé
    • Supplementary information form
    • Transcript(s)
  • References 
    • Number of references:  3
    • Type of references: 

      normally from academic sources
  • English language proficiency (ELP) (if applicable)

Degree requirements

    Master's Research Paper option:

  • Graduate Academic Integrity Module (Graduate AIM)
  • Courses 
    • Students are required to take 6 (0.50 unit weight) courses from lists A and B. At least 4 courses must be taken from list A. At most 2 of the 6 courses taken may be courses in which undergraduate students predominate.
    • List A Core Courses:
      • CM 730/CS 687 Introduction to Symbolic Computation
      • 1 of CM 740/CO 602/CS 795 Fundamentals of Optimization; CO 666 Continuous Optimization
      • CM 750/AMATH 741/CS 778 Numerical Solution of Partial Differential Equations
      • 1 of CM 761/STAT 840 Computational Inference; CM 762/STAT 842 Data Visualization; CM 763/STAT 841 Statistical Learning - Classification; CM 764/STAT 844 Statistical Learning - Advanced Regression
      • CM 770/AMATH 740/CS 770 Numerical Analysis
    • List B Courses (some are held with undergraduate courses):
      • CO 650 Combinatorial Optimization
      • CO 652 Integer Programming
      • CO 663 Convex Optimization and Analysis
      • CO 671 Semi-definite Optimization
      • CO 673/CS 794 Optimization for Data Science
      • CO 681/CS 768/PHYS 767/QIC 710/AMATH 871/PMATH 871 Quantum Information Processing
      • CO 685 The Mathematics of Public-Key Cryptography
      • CO 687 Applied Cryptography
      • CO 778/ACTSC 973 Portfolio Optimization
      • CO 781 Topics in Quantum Information
      • CS 666 Algorithm Design and Analysis
      • CS 675 Computational Linear Algebra
      • CS 676 Numeric Computation for Financial Modelling
      • CS 679 Neural Networks
      • CS 680 Introduction to Machine Learning
      • CS 682 Computational Techniques in Biological Sequence Analysis
      • CS 684 Computational Vision
      • CS 686 Introduction to Artificial Intelligence
      • CS 688 Introduction to Computer Graphics
      • CS 763 Computational Geometry
      • CS 765/CO 785 Algorithmic Number Theory
      • CS 774 Advanced Computational Finance
      • CS 775 Parallel Algorithms in Scientific Computing
      • CS 780 Advanced Symbolic Computation
      • CS 786 Probabilistic Inference and Machine Learning
      • CS 787 Computational Vision
      • CS 867 Advanced Topics in Quantum Computing
      • STAT 901 Theory of Probability 1
      • STAT 940 Deep Learning
      • ACTSC 970/ACC 770 Finance 1
      • AMATH 655 Control Theory
      • AMATH 663 Fluid Mechanics
      • AMATH 677 Stochastic Processes for Applied Mathematics
      • AMATH 731 Applied Functional Analysis
      • AMATH 753 Advanced Partial Differential Equations
      • AMATH 777 Stochastic Processes in the Physical Sciences
      • AMATH 840 Advanced Numerical Methods for Computational and Data Sciences
      • AMATH 841 Finite Element Methods
      • AMATH 881 Introduction to Mathematical Oncology
      • AMATH 882 Mathematical Cell Biology
      • Any other course at this level approved by the graduate committee.
    • The courses listed above are regularly offered within the Faculty. Other advanced courses are offered within the Faculty of Mathematics on topics of computational mathematics on a more irregular basis. These courses may be taken with approval of the Graduate Committee. Similarly, courses offered outside the Faculty, in Computational Mathematics or in some area of its application may be approved by the Graduate Committee.
    • Students with strong backgrounds in some core areas may be granted exemption from the corresponding core courses required by the program; in each such case another course will be substituted for the exempted course so that the total courses required remains the same.
    • Students must maintain an average of 70% in order to remain in good standing. Formal progress reports will be required in the event that a student wishes or needs to remain in the program longer than one year for the Master's Research Paper option.
  • Link(s) to courses
  • Graduate Studies Work Report
    • In Computational Mathematics, a master’s option may be undertaken on a co-operative basis, enabling a student to combine graduate studies with some work experience. The program involves an initial study period, a work period and a final study period. It is fairly flexible in length, each period comprising one or more terms. The usual pattern of study and work consists of two academic terms in which some or all of the courses are completed, a one or two-term work placement, and a final academic term in which the research paper, or coursework is completed. The student is encouraged to complete COOP 601 Career Success Strategies in the term before the start of their work term. Admission to the co-op program is competitive. Students should apply for this option in their first term, and admittance will be decided based on the students first term marks.
    • The student will be required to do a one or two-term work placement at a suitable industrial location, to begin as soon as possible after the coursework, or 50% of the degree requirements have been completed. The student will also be expected to return to campus after the work placement in order to complete the research paper or remaining coursework. The student will be required to provide a work term report when they return to campus.
  • Master’s Research Paper
    • Students must undertake an independent research project culminating in a research paper (1.00 unit weight). It is intended that the research project will be approximately the equivalent of two full courses and will be conducted under the direction of the student’s research supervisor. To be successfully completed, the research paper must be unanimously approved by the student's advisory committee, consisting of the student’s research supervisor and one additional reader.
    • Students are also required to give a presentation in their final term on their research paper.

    Coursework option:

  • Graduate Academic Integrity Module (Graduate AIM)
  • Courses 
    • The Coursework option requires 8 one-term graduate courses from lists A and B (with a unit weight of 0.50). At least 4 of these courses must be from list A. At most 3 of the 8 courses taken may be courses in which undergraduate students predominate.
    • List A Core Courses:
      • CM 730/CS 687 Introduction to Symbolic Computation
      • 1 of CM 740/CO 602/CS 795 Fundamentals of Optimization; CO 666 Continuous Optimization
      • CM 750/AMATH 741/CS 778 Numerical Solution of Partial Differential Equations
      • 1 of CM 761/STAT 840 Computational Inference; CM 762/STAT 842 Data Visualization; CM 763/STAT 841 Statistical Learning - Classification; CM 764/STAT 844 Statistical Learning - Advanced Regression
      • CM 770/AMATH 740/CS 770 Numerical Analysis
    • List B Courses (some are held with undergraduate courses):
      • CO 650 Combinatorial Optimization
      • CO 652 Integer Programming
      • CO 663 Convex Optimization and Analysis
      • CO 671 Semi-definite Optimization
      • CO 673/CS 794 Optimization for Data Science
      • CO 681/CS 768/PHYS 767/QIC 710/AMATH 871/PMATH 871 Quantum Information Processing
      • CO 685 The Mathematics of Public-Key Cryptography
      • CO 687 Applied Cryptography
      • CO 778/ACTSC 973 Portfolio Optimization
      • CO 781 Topics in Quantum Information
      • CS 666 Algorithm Design and Analysis
      • CS 675 Computational Linear Algebra
      • CS 676 Numeric Computation for Financial Modelling
      • CS 679 Neural Networks
      • CS 680 Introduction to Machine Learning
      • CS 682 Computational Techniques in Biological Sequence Analysis
      • CS 684 Computational Vision
      • CS 686 Introduction to Artificial Intelligence
      • CS 688 Introduction to Computer Graphics
      • CS 763 Computational Geometry
      • CS 765/CO 785 Algorithmic Number Theory
      • CS 774 Advanced Computational Finance
      • CS 775 Parallel Algorithms in Scientific Computing
      • CS 780 Advanced Symbolic Computation
      • CS 786 Probabilistic Inference and Machine Learning
      • CS 787 Computational Vision
      • CS 867 Advanced Topics in Quantum Computing
      • STAT 901 Theory of Probability 1
      • STAT 940 Deep Learning
      • ACTSC 970/ACC 770 Finance 1
      • AMATH 655 Control Theory
      • AMATH 663 Fluid Mechanics
      • AMATH 677 Stochastic Processes for Applied Mathematics
      • AMATH 731 Applied Functional Analysis
      • AMATH 753 Advanced Partial Differential Equations
      • AMATH 777 Stochastic Processes in the Physical Sciences
      • AMATH 840 Advanced Numerical Methods for Computational and Data Sciences
      • AMATH 841 Finite Element Methods
      • AMATH 881 Introduction to Mathematical Oncology
      • AMATH 882 Mathematical Cell Biology
      • Any other course at this level approved by the graduate committee.
    • The courses listed above are regularly offered within the Faculty. Other advanced courses are offered within the Faculty of Mathematics on topics of computational mathematics on a more irregular basis. These courses may be taken with approval of the Graduate Committee. Similarly, courses offered outside the Faculty, in Computational Mathematics or in some area of its application may be approved by the Graduate Committee.
    • Students with strong backgrounds in some core areas may be granted exemption from the corresponding core courses required by the program; in each such case another course will be substituted for the exempted course so that the total courses required remains the same.
    • Students must maintain an average of 70% in order to remain in good standing. Formal progress reports will be required in the event that a student wishes or needs to remain in the program longer than two years for the Coursework option.
  • Link(s) to courses
  • Graduate Studies Work Report
    • In Computational Mathematics, a master’s option may be undertaken on a co-operative basis, enabling a student to combine graduate studies with some work experience. The program involves an initial study period, a work period and a final study period. It is fairly flexible in length, each period comprising one or more terms. The usual pattern of study and work consists of two academic terms in which some or all of the courses are completed, a one or two-term work placement, and a final academic term in which the research paper, or coursework is completed. The student is encouraged to complete COOP 601 Career Success Strategies in the term before the start of their work term. Admission to the co-op program is competitive. Students should apply for this option in their first term, and admittance will be decided based on the students first term marks.
    • The student will be required to do a one or two-term work placement at a suitable industrial location, to begin as soon as possible after the coursework, or 50% of the degree requirements have been completed. The student will also be expected to return to campus after the work placement in order to complete the research paper or remaining coursework. The student will be required to provide a work term report when they return to campus.
Computational Mathematics website