MSc Computer Science - Data Science

A fully-qualified student may complete a Master's in Data Science by undertaking 30 credits of coursework. Students in this route who are interested in pursuing the Co-op Designation must complete CS 700, 710, 711, 712, 713, 714, 715, and 716 before they can undertake any co-op work terms.

MSc in Computer Science - Data Science (30 credit hours) (effective 202130)

This program will be run with one cohort of students each year. The application deadline for this program is February 15, for admissions in the Fall semester. There will be no admissions in the other semesters. If the application deadline is missed or if an accepted student is not able to start in the Fall semester, they will need to wait until the next year and join the next cohort of students.

This program will run with a strict curriculum of the following courses:

First Semester (Fall)

                        CS 700: Software Development Fundamentals (3)

                        CS 710: Python & Data Fundamentals (3)

            Second Semester (Winter)

                        CS 711: Foundations of Data Science (3)

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

            Third Semester (Spring/Summer)

                        CS 713: Applied Machine Learning (3)

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

            Fourth Semester (Fall)

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

                        CS 716: Communication in Data Science (3)

            Fifth Semester (Winter)

                       CS 718: Data Science Seminar (0)

                       CS 719: Data Science Project (6)

The detailed course descriptions are provided below:

CS 700: Software Development Fundamentals (3)

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 for all streams in the MSc Course Route.            

CS 710: Python & Data Fundamentals (3)

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.

CS 711: Foundations of Data Science (3)

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)

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. 

CS 713: Applied Machine Learning (3)

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)

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.

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

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)

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.

CS 718: Data Science Seminar (0)

Co-op Designation Requirements

A co-op designation may be added to this graduate program, under the following conditions:

• an application to the co-op designation may be made at the end of the third semester (after the completion of
  CS 700, 710, 711, 712, 713, and 714)
• a minimum average grade of 80% or higher must have been acheived on the courses taken in the first three semesters of the program
• the arragement of a co-op placement will be done in collaboration with the Career Centre, during the fourth semester
• a minimum average grade of 80% or higher must be maintained through the fourth semester of the program (CS 715, 716)
• the co-op placements will occur during the fifth and sixth semesters; these can be with the same employer or different ones
• if two semesters of work terms are completed, a co-op designation will be added to your academic record
• the normal coursework of the fifth semester (CS 719) will take place after the end of the co-op placements (in the sixth or seventh semester, depending on whether one or two co-op terms are taken)

• students who are on co-op placements must complete CS 718 – Data Science Seminar course in their first co-op term

  
  Note: Acceptance to a Co-op work term is subject to the approval of the Co-op Grad Coordinator. Attaining a Co-op work term is a competitive process through the Career Centre. Credit hours earned on a work term are extra to the credit hours required in this program.