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 optimization
• 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