MSc in Computer Science – Data Science

A fully-qualified student may complete a Master's in Data Science by undertaking 30 credits of coursework. This program has a strict curriculum of the following courses:

First Semester (Fall)

CS 700: Software Development Fundamentals (3 cr)

Modern software development principles and practices. Topics include modern software development fundamentals and methodologies, unit testing, source code control, teamwork, and modern programming languages, frameworks, software development tools, and environments. 

 Note: This course is common to both professionally-focused MSc programs

CS 710: Python & Data Fundamentals (3 cr)

Data-centred programming in Python. Topics include Python fundamentals, object-oriented design, data modelling, advanced data structures, extract, transform, and load (ETL) philosophy, data-centred libraries (e.g., Pandas, NumPy, SciPy, scikit-learn), SQL databases, No-SQL databases, statistical analysis tools.

Second Semester (Winter)

CS 711: Foundations of Data Science (3 cr)

Broad overview of the data science process lifecycle and methods. Topics include data ethics, data discovery, data preparation, model planning, machine learning model implementation, and evaluation, visualization, and delivery.

CS 712: Foundations of Statistics & Machine Learning (3 cr)

Statistical basis for machine learning. Topics include distributions, probabilities, sampling, hypothesis testing, Bayes’ theorem, maximum likelihood, machine learning theory, classes of machine learning, linear regression, kernel methods, dimensional reduction, gradient descent, ensemble techniques, and neural networks. 

Third Semester (Spring/Summer)

CS 713: Applied Machine Learning (3 cr)

Machine learning approaches applied to real-world problems. Topics include classification, regression, clustering, decision trees and random forests, Bayesian networks, deep learning, face and object recognition, time-series forecasting, anomaly detection, natural language processing, and machine translation.

CS 714: Big Data Analytics & Cloud Computing (3 cr)

Techniques for performing big data analytics within a cloud environment. Topics include foundations of cloud computing, containers, micro-services, distributed file systems, MapReduce, real-time data processing, scale-up, scale-out, and cloud-based machine learning. Students will undertake a milestone-based project using Microsoft Azure, Amazon Web Services, Google Cloud, or some other cloud platform.

Fourth Semester (Fall)

CS 715: Advanced Data Science & Machine Learning (3 cr)

State-of-the-art in data science and machine learning. Topics may include the latest advancements in reinforcement learning, deep learning, spatio-temporal forecasting, and natural language processing. Students will pursue real-world data science project that employs the latest machine learning methods and techniques.

CS 716: Communication in Data Science (3 cr)

Mechanisms for communication within Data Science projects. Topics include communication fundamentals, visualization fundamentals, data science notebooks, and visualization libraries. Students will be expected to communicate information about a data science project in four different modes: structured abstract, poster, project notebook, and oral presentation.

Fifth Semester (Winter)

CS 718: Data Science Seminar (0 cr)

Data Science students will attend a professionally-focused seminar series with topics including entrepreneurship, ethics, intellectual property, innovation, start-up culture, and EDI.

CS 719: Data Science Project (6 cr)

A milestone-based project will be pursued, serving as a capstone for the Data Science Stream. Final projects will be demonstrated and presented in a public venue.

A co-op designation may be added to this graduate program. See our Graduate Co-op Program page for more information