Master of Data Science and Artificial Intelligence (MDSAI)

The program information below was valid for the fall 2021 term (September 1, 2021 - December 31, 2021). 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
  • Winter
  • Spring
• Delivery mode 
  • On-campus
• Length of program 
  • Full-time: 3 terms/12 months
  • Part-time: 9 terms/36 months
• Program type 
  • Master's
  • Professional
• Registration option(s) 
  • Full-time
  • Part-time
• Registration option(s) information 
  • Note direct entry into the Master of Data Science and Artificial Intelligence (MDSAI) program is only available through the part-time option.
  • Note that part-time students starting in Winter or Spring will need to consider course sequencing options since some courses are not offered every term.
• Study option(s) 
  • Coursework

• Minimum requirements 
  • 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 at least one of computer science or statistics.
  • Students in the Master of Data Science and Artificial Intelligence - Co-operative Program can apply to transfer into the Master of Data Science and Artificial Intelligence Program after completing at least one academic term. Admittance will be decided by the Graduate Director on a case-by case basis.
• Application materials 
  • Résumé/Curriculum Vitae
  • Supplementary information form
  • Transcript(s)
• References 
  • Number of references:  3
  • Type of references: 

    at least 2 academic sources

• English language proficiency (ELP) (if applicable)

Coursework option:

Graduate Academic Integrity Module (Graduate AIM)

Courses 

• Students must complete at least 9 courses: normally 1 foundation course, 5 core courses, and 3 elective courses.

• Foundation courses

  • Students are expected to take at most 1 of the following 2 foundational courses depending on their undergraduate major:

    • CS 600 Fundamentals of Computer Science for Data Science (designed for non-CS major background students)

    • STAT 845 Statistical Concepts for Data Science (designed for non-STAT major background students)

• Core courses

  • Students are required to take the following core courses:

  • STAT 847 Exploratory Data Analysis

  • 1 of:

    • CS 651 Data-Intensive Distributed Computing (designed for CS major background students), or

    • CS 631 Data-Intensive Distributed Analytics (designed for non-CS major background students)

  • 1 of:

    • STAT 841 Statistical Learning - Classification

    • STAT 842 Data Visualization

    • STAT 844 Statistical Learning - Function Estimation

  • 1 of:

    • CS 638 Principles of Data Management and Use

    • CS 648 Database Systems Implementation

    • CS 680 Introduction to Machine Learning

    • CS 685 Machine Learning: Statistical and Computational Foundations

  • 1 of:

    • CO 602 / CS 795 Fundamentals of Optimization

    • CO 673 / CS 794 Optimization for Data Science

    • CO 663 Convex Optimization and Analysis

• Elective courses

  • Students must take enough additional elective courses to fulfill the 9-course requirement. These courses must normally be taken from the following list of selected graduate courses. Courses not on this list are subject to the approval of the Graduate Director.

    • CO 602 / CS 795 Fundamentals of Optimization

    • CO 673 / CS 794 Optimization for Data Science

    • CO 650 Combinatorial Optimization

    • CO 663 Convex Optimization and Analysis

    • CO 769 Topics in Continuous Optimization(*)

    • CS 638 Principles of Data Management and Use

    • CS 648 Database Systems Implementation

    • CS 654 Distributed Systems

    • CS 680 Introduction to Machine Learning

    • CS 685 Machine Learning: Statistical and Computational Foundations

    • CS 686 Introduction to Artificial Intelligence

    • CS 740 Database Engineering

    • CS 742 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 885 Advanced Topics in Computational Statistics(*)

    • CS 886 Advanced Topics in Artificial Intelligence

    • 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(*)

    • DS 701/702 Data Science Project 1 & 2

    • 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 or Artificial Intelligence; they are subject to the approval of the Graduate Officer.

• In order to remain in good academic standing, students must maintain an average of 75% and a minimum grade of 70% in all their courses. Progress reports are not required; however, the Director will review students’ overall average every term. Students whose average falls below the program’s minimum requirements may be required to withdraw from the program. The minimum average required by the program is higher than the university’s minimum requirement (70%).

Link(s) to courses

• Combinatorics and Optimization (CO) courses
• Computer Science (CS) courses
• Statistics (STAT) courses

Ethics Workshop

• Students must complete a 3-day workshop on “Ethics in Data Science and Artificial Intelligence” that will be offered once a year. Alternatively, students can complete the course CS 798 Advanced Research Topics on “Artificial Intelligence: Law, Ethics, and Policy’’.