Master of Mathematics (MMath) in Data Science

The program information below is valid for the winter 2020 term (January 1, 2020 - April 30, 2020).

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.

  • Admit term(s) 
    • Fall
  • Delivery mode 
    • On-campus
  • Length of program 
    • 4 to 6 terms
  • Program type 
    • Master's
    • Research
  • Registration option(s) 
    • Full-time
    • Part-time
  • Study option(s) 
  • Minimum requirements 
    • A four-year Honours Bachelor’s degree or equivalent in data science, computer science, statistics, mathematics or a related field, with a minimum overall average of 78%.
    • Experience at the senior level in computer science or statistics.
  • Application materials 
    • Résumé/Curriculum Vitae
    • Supplementary information form
    • Transcript(s)
  • References 
    • Number of references:  3
    • Type of references: 

      at least 2 academic

  • English language proficiency (ELP) (if applicable)

    Thesis option:

  • Graduate Academic Integrity Module (Graduate AIM)
  • Courses 
    • Students must complete at least 4 courses. Students lacking adequate background in computer science may be required to take CS 600 Fundamentals of Computer Science for Data Science, and students lacking adequate background in statistics may be required to take STAT 845 Statistical Concepts for Data Science. Neither of these courses may be counted toward the 4 course requirement. The 4 courses must include:
      • 1. STAT 847 Exploratory Data Analysis
      • 2. Exactly 1 of:
        • CS 631 Data-Intensive Distributed Analytics, or
        • CS 651 Data-Intensive Distributed Computing
      • 3. At least 1 of:
        • CS 680 Introduction to Machine Learning
        • CS 685 Machine Learning: Statistical and Computational Foundations
        • CS 686 Introduction to Artificial Intelligence
        • CS 795 / CO 602 Fundamentals of Optimization
        • CS 794 / CO 673 Optimization for Data Science
        • CO 650 Combinatorial Optimization
        • CO 663 Convex Optimization and Analysis
        • CS 786 Probabilistic Inference and Machine Learning
        • CS 886 Advanced Topics in Artificial intelligence
        • STAT 840 Computational Inference
        • STAT 841 Statistical Learning - Classification
        • STAT 844 Statistical Learning - Function Estimation
        • STAT 946 Topics in Probability and Statistics(*)
      • 4. The fourth course is normally chosen from the following list:
      • Machine learning / statistical learning / optimization
        • CS 680 Introduction to Machine Learning
        • CS 685 Machine Learning: Statistical and Computational Foundations
        • CS 686 Introduction to Artificial Intelligence
        • CS 795/CO 602 Fundamentals of Optimization
        • CS 794/CO 673 Optimization for Data Science
        • CO 650 Combinatorial Optimization
        • CO 663 Convex Optimization and Analysis
        • CO 769 Topics in Continuous Optimization(*)
        • CS 786 Probabilistic Inference and Machine Learning
        • CS 885 Advanced Topics in Computational Statistics(*)
        • CS 886 Advanced Topics in Artificial intelligence
        • STAT 840 Computational Inference
        • STAT 841 Statistical Learning - Classification
        • STAT 844 Statistical Learning - Function Estimation
        • STAT 946 Topics in Probability and Statistics(*)
      • Computer systems and databases
        • CS 638 Principles of Database Management and Use
        • CS 648 Database Systems Implementation
        • CS 656 Computer Networks
        • CS 657 System Performance Evaluation
        • CS 658 Computer Security and Privacy
        • CS 740 Database Engineering
        • CS 741 Non-Traditional Databases
        • CS 742 Parallel and Distributed Database Systems
        • CS 743 Principles of Database Management and Use
        • CS 755 Systems and Network Architectures and Implementation
        • CS 848 Advanced Topics in Databases(*)
      • Distributed computing
        • CS 654 Distributed Systems
        • CS 856 Advanced Topics in Distributed Computing(*)
      • Data exploration
        • STAT 842 Data Visualization
      • Other
        • CS 798 Advanced Research Topics(*)
      • (*) Note: CO 769, CS 798, CS courses at the 800 level, and STAT courses at the 900 level should be on a topic in Data Science; they are subject to the approval of the Graduate Director.
  • Link(s) to courses
  • Ethics Workshop
    • Students must complete a 3-day workshop on “Ethics in Data Science and Artificial Intelligence” that will be offered in the Fall term. Alternatively, students can complete the course CS 798 Advanced Research Topics on “Artificial Intelligence: Law, Ethics, and Policy’’.
  • Master’s Thesis
    • Students must complete a thesis in the field of data science, under the supervision of a faculty member in Data Science. The student must also complete a 20-minute oral presentation.