Graduate Studies and Postdoctoral Affairs (GSPA)
Needles Hall, second floor, room 2201
The program information below was valid for the fall 2019 term (September 1, 2019 - December 31, 2019). 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.
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%).
Needles Hall, second floor, room 2201
The University of Waterloo acknowledges that much of our work takes place on the traditional territory of the Neutral, Anishinaabeg and Haudenosaunee peoples. Our main campus is situated on the Haldimand Tract, the land granted to the Six Nations that includes six miles on each side of the Grand River. Our active work toward reconciliation takes place across our campuses through research, learning, teaching, and community building, and is co-ordinated within the Office of Indigenous Relations.