The program information below is valid for the fall 2019 term (September 1, 2019 - December 31, 2019).

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.

Graduate research fields

  • Computational Statistics
  • Finance
  • Industrial Statistics
  • Probability
  • Statistical Theory and Methods
  • Minimum requirements 
    • A four-year Honours Bachelor degree with a significant statistics and/or actuarial science component.
    • An overall 78% average from a Canadian university (or its equivalent).
    • An interview may be required.
  • 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)

    Thesis option:

  • Graduate Academic Integrity Module (Graduate AIM)
  • Courses 
    • Students must complete 4 one-term (0.50 unit weight) courses with an overall average of at least 70%.
    • The 4 courses must include STAT 850 Estimation and Hypothesis Testing and at least 2 900-level STAT courses.
  • Link(s) to courses
  • Graduate Skills Workshop
  • Master’s Thesis
    • Students must complete a thesis and an oral presentation.

    Master's Research Paper option:

  • Graduate Academic Integrity Module (Graduate AIM)
  • Courses 
    • Students must complete 7 one-term (0.50 unit weight) courses with an overall average of at least 70%.
    • 3 of the 7 required courses should include:
      • STAT 830 Experimental Design or STAT 835 Statistical Methods for Process Improvement
      • STAT 850 Estimation and Hypothesis Testing
      • STAT 854 Sampling Theory and Practice
    • Exemptions can be made to these required courses at the discretion of the Associate Chair for Graduate Studies.
  • Link(s) to courses
  • Graduate Skills Workshop
  • Master’s Research Paper
    • Students must complete a research paper that will be given a numeric grade which appears on the transcript beside the milestone.

    Coursework option:

    The coursework option includes a specialization in Data Science.

    Note: The Department of Statistics and Actuarial Science is not currently accepting applications for the coursework option.

  • Graduate Academic Integrity Module (Graduate AIM)
  • Courses 
    • Students must complete 8 one-term (0.50 unit weight) graduate courses [with an overall average of at least 70%] from the Data Science lists of courses.
    • Students should take a minimum of 4 STAT courses, and no courses which are neither STAT nor Computer Science (CS).
    • Students must satisfy the following course requirements:
    • Foundation course:
      • CS 600 Fundamentals of Computer Science for Data Science
    • Students with a STAT major degree are expected to take the foundation course CS 600. However, STAT major students will be exempted from taking CS 600 if they have a sufficient background in Computer Science; instead they will be required to take another CS course from the elective course list.
    • Required core courses:
      • STAT 847 Exploratory data analysis
      • CS 631 Data-Intensive Distributed Analytics
    • 1 of the following required breadth courses:
      • STAT 841 Statistical Learning: Classification
      • STAT 842 Data Visualization
      • STAT 844 Statistical Learning: Function estimation
    • 4 elective courses from the following list:
      • STAT 840 Computational Inference
      • STAT 841 Statistical Learning: Classification
      • STAT 842 Data Visualization
      • STAT 844 Statistical Learning: Function estimation
      • STAT 946 Topics in Probability and Statistics
      • CS 638 Principles of Data Management and Use
      • CS 648 Database Systems Implementation
      • CS 654 Distributed Systems
      • CS 658 Computer Security and Privacy
      • CS 680 Introduction to Machine Learning
      • CS 685 Machine Learning Theory: Statistical and Computational Foundations
      • CS 686 Introduction to Artificial Intelligence
      • CS 740 Database Engineering
      • CS 741 Parallel and Distributed Database Systems
      • CS 743 Principles of Database Management and Use
      • CS 786 Probabilistic Inference and Machine Learning
      • CS 798 Advanced Research Topics
      • CS 848 Advanced Topics in Databases
      • CS 856 Advanced Topics in Distributed Computing
      • CS 858 Advanced Topics in Cryptography, Security and Privacy
      • CS 870 Advanced Topics in Scientific Computing
      • CS 886 Advanced Topics in Artificial Intelligence
    • Note: 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 Officer.
  • Link(s) to courses
  • Data Science Requirement
    • Students must complete the required core data science courses in order to satisfy the Data Science Requirement milestone.