Master of Mathematics (MMath) in Statistics

The program information below was valid for the fall 2017 term (September 1, 2017 - December 31, 2017). 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.

• Admit term(s) 
  • Fall
• Delivery mode 
  • On-campus
• Program type 
  • Master's
  • Research
• Registration option(s) 
  • Full-time
  • Part-time
• Study option(s) 
  • Thesis
  • Master's Research Paper
  • Coursework

• 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).
• 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

• Statistics (STAT) 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

• Statistics (STAT) 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.

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

• Statistics (STAT) courses