Master of Mathematics (MMath) in Data Science

The program information below was valid for the winter 2024 term (January 1, 2024 - April 30, 2024). 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 
    • 4 to 6 terms
  • Program type 
    • Master's
    • Research
  • Registration option(s) 
    • Full-time
    • Part-time
  • Study option(s) 

Admission requirements

  • 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)

Degree requirements

    All students are admitted to the Thesis study option. Students can transfer to the Master’s Research Paper study option with approval from their thesis supervisor and the Graduate Director.

    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 normally 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 / CM 740 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 / CM 761 Computational Inference
        • STAT 841 / CM 763 Statistical Learning - Classification
        • STAT 844 / CM 764 Statistical Learning - Advanced Regression
        • 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 / CM 740 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 / CM 761 Computational Inference
        • STAT 841 / CM 763 Statistical Learning - Classification
        • STAT 844 / CM 764 Statistical Learning - Advanced Regression
        • 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 / CM 762 Data Visualization
      • Other
        • CS 798 Advanced Research Topics(*)
      • At the discretion of the Data Science Committee, substitutions may be allowed.
      • (*) 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 containing original work under the supervision of a faculty member. The thesis Examining Committee consists of the thesis supervisor and two additional readers. The supervisor and first reader should be members of the Data Science program; the second reader may be any regular faculty member of the University. The student must make an oral presentation on the thesis before Examining Committee members following the regulations for the MMath degree by the Faculty of Mathematics. Committee members should receive a copy of the thesis at least two weeks prior to presentation.

    Master's Research Paper option:

  • Graduate Academic Integrity Module (Graduate AIM)
  • Courses 
    • Students must complete at least 6 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 6 course requirement. The 6 courses must normally 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 / CM 740 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 / CM 761 Computational Inference
        • STAT 841 / CM 763 Statistical Learning - Classification
        • STAT 844 / CM 764 Statistical Learning - Advanced Regression
        • STAT 946 Topics in Probability and Statistics(*)
      • 4. The 3 additional courses are 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 / CM 740 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 / CM 761 Computational Inference
        • STAT 841 / CM 763 Statistical Learning - Classification
        • STAT 844 / CM 764 Statistical Learning - Advanced Regression
        • 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 / CM 762 Data Visualization
      • Other
        • CS 798 Advanced Research Topics(*)
      • At the discretion of the Data Science Committee, substitutions may be allowed.
      • (*) 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 Research Paper
    • Students must complete a research paper under the supervision of a faculty member. The supervisor plus one additional regular faculty member of the University must assess and approve the paper. Students must also present their research paper topic in a publicly announced seminar.
Data Science website
Master of Mathematics (MMath) in Data Science future students program page