The following list includes courses specifically designed for the Data Science graduate program as well as relevant courses taught by various departments in the Faculties of Science, Business and Medicine.

Please note: This list is not exhaustive. Elective courses may not be offered on a yearly basis. 

Required Courses

DSCI-5001 Statistical inference for data science (propaedeutic)

DSCI-5002 Basics of Python and R (propaedeutic)

DSCI-5003 Linear algebra for regression analysis (propaedeutic)

DSCI-6619 Regression models

DSCI-6659 Statistical exploration of data

DSCI-6601 Practical machine learning

DSCI-6602 Deep learning and artificial intelligence

DSCI-6607 Programmatic data analysis using Python and R

DSCI-6690 Data science case study series (2 credit hours)

DSCI-695A/B Capstone project (2 credit hours)

Elective Courses

BUSI-8025 Information systems

BUSI-9021 Data management

BUSI-9022 Information systems analysis and design

BUSI-9912 Probabilistic models

CMCS-6950 Computer based tools and applications

COMP-6907 Data mining techniques and methodology

COMP-6908 Database technology and applications

COMP-6917 Complex networks

COMP-6934 Introduction to data visualization

DSCI-6650 Reinforcement learning

MATH-6100 Dynamical systems

MATH-6201 Numerical methods for time-dependent differential equations

MATH-6202 Nonlinear and linear optimisation

MATH-6204 Iterative methods in numerical linear algebra

MATH-6210 Numerical solutions of differential equations

MATH-6351 Advanced linear algebra

MED-6260 Applied data analysis for clinical epidemiology

MED-6278 Advanced biostatistics for health research

STAT-6503 Stochastic processes

STAT-6505 Survival analysis

STAT-6530 Longitudinal data analysis

STAT-6545 Computational statistics

STAT-6561 Categorical data analysis

STAT-6563 Sampling theory

STAT-6564 Experimental designs

STAT-6571 Financial and environmental time series

STAT-6573 Statistical genetics