Data Science (Gr. Cert.) (15 credits)
Offered by: Mathematics and Statistics (Faculty of Science)
Program credit weight: 15-16
Program Description
The Graduate Certificate in Data Science provides training in the principles, methods, and applications of data science, including statistical analysis, machine learning, and the interpretation of complex data in modern data-driven environments.
Required Courses (12 credits)
| Course | Title | Credits |
|---|---|---|
| COMP 551 | Applied Machine Learning. | 4 |
Applied Machine Learning. Terms offered: Fall 2026, Winter 2027 Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems. | ||
| COMP 570 | Fundamentals of Data Science | 4 |
Fundamentals of Data Science Terms offered: Fall 2026 Comprehensive introduction to the data science process. Orientation to the use and configuration of core data science toolkits, data collection and annotation fundamentals, principles of responsible data science, the use of quantitative tools in data science, and presentation of data science findings. Large-scale team-based projects. | ||
| MATH 682 | Statistical Inference. | 4 |
Statistical Inference. Terms offered: Fall 2026 Conditional probability and Bayes’ Theorem, discrete and continuous univariate and multivariate distributions, conditional distributions, moments, independence of random variables. Modes of convergence, weak law of large numbers, central limit theorem. Point and interval estimation. Likelihood inference. Bayesian estimation and inference. Hypothesis testing. | ||
Complementary Courses (3-4 credits)
3-4 credits from the following:
| Course | Title | Credits |
|---|---|---|
| INFS 630 | Data Mining. | 3 |
Data Mining. Terms offered: Fall 2026 Introduction to data mining. Topics include datapreprocessing, data warehouse architecture, onlineanalytical processing (OLAP), online analyticalmining (OLAM), basic concepts and methods offrequent patterns mining, association rules mining,classification analysis, cluster analysis, and textmining. | ||
| INFS 685 | Artificial Intelligence for Cybersecurity | 3 |
Artificial Intelligence for Cybersecurity Terms offered: this course is not currently offered. Exploration of the fusion of artificial intelligence (AI) and cybersecurity, offering insights into data science basics, interpretability, and ethical AI. Topics include: feature engineering, anomaly detection, ethical considerations and secure practices in cyber defence strategies. | ||
| MATH 533 | Regression and Analysis of Variance. | 4 |
Regression and Analysis of Variance. Terms offered: Fall 2026 Multivariate normal and chi-squared distributions; quadratic forms. Multiple linear regression estimators and their properties. General linear hypothesis tests. Prediction and confidence intervals. Asymptotic properties of least squares estimators. Weighted least squares. Variable selection and regularization. Selected advanced topics in regression. Applications to experimental and observational data. | ||
| MATH 545 | Introduction to Time Series Analysis. | 4 |
Introduction to Time Series Analysis. Terms offered: Winter 2027 Stationary processes; estimation and forecasting of ARMA models; non-stationary and seasonal models; state-space models; financial time series models; multivariate time series models; introduction to spectral analysis; long memory models. | ||
| MATH 559 | Bayesian Theory and Methods. | 4 |
Bayesian Theory and Methods. Terms offered: Fall 2026 Subjective probability, Bayesian statistical inference and decision making, de Finetti’s representation. Bayesian parametric methods, optimal decisions, conjugate models, methods of prior specification and elicitation, approximation methods. Hierarchical models. Computational approaches to inference, Markov chain Monte Carlo methods, Metropolis—Hastings. Nonparametric Bayesian inference. | ||
| MATH 680 | Computation Intensive Statistics. | 4 |
Computation Intensive Statistics. Terms offered: Fall 2026 General introduction to computational methods in statistics; optimization methods; EM algorithm; random number generation and simulations; bootstrap, jackknife, cross-validation, resampling and permutation; Monte Carlo methods: Markov chain Monte Carlo and sequential Monte Carlo; computation in the R language. | ||
Students may take a 500-level course outside the department with the Graduate Program Director’s approval.