Program overview
Admission Requirements
Intakes

Last updated on 2025-04-24

Program overview

Program Overview

The Master of Computer Science in Data Science, Analytics, and Artificial Intelligence (DSAAI) is designed to provide students with a comprehensive understanding of the technical aspects of data science. This program is a natural extension of the popular Collaborative Specialization in Data Science, focusing on interdisciplinary approaches. Students will engage in groundbreaking research across various methodologies, including statistics, AI methods, and software engineering. The program emphasizes the collection, preprocessing, exploration, and visualization of data from diverse sources, preparing graduates for a dynamic career in data science and analytics.

Program Structure

The Master of Computer Science in Data Science, Analytics, and Artificial Intelligence is structured to be completed in 2 years. The program offers various pathways, including:

  • Coursework
  • Research Project
  • Thesis (depending on the program)

Students will benefit from a co-op option, allowing them to gain practical experience in the field. The program is offered in the Fall term, with an application deadline of February 1. This structure ensures that students receive a well-rounded education while also having the opportunity to apply their knowledge in real-world settings.

Featured Experiences

  • Co-op opportunities to gain hands-on experience in the tech sector.
  • Interdisciplinary approach, combining insights from the Faculty of Science and Faculty of Engineering and Design.
  • Access to advanced research areas, including data mining, predictive analytics, and machine learning.
  • Exposure to both stored and streaming data analysis.
  • Utilization of data analytics platforms and parallel processing techniques.

Career Options

Graduates of the Master of Computer Science in Data Science, Analytics, and Artificial Intelligence can pursue a variety of career paths in Canada, including:

  • Data Scientist: Analyze complex data sets to derive actionable insights and inform business decisions.
  • Data Analyst: Collect, process, and perform statistical analyses on large datasets to identify trends and patterns.
  • Machine Learning Engineer: Develop algorithms and models that enable machines to learn from data and make predictions.
  • Business Intelligence Analyst: Use data analysis tools to help organizations make strategic decisions based on data-driven insights.
  • Data Engineer: Design and maintain the architecture that allows for the collection and processing of data at scale.

 

DISCLAIMER: The information above is subject to change. For the latest updates, please contact LOA Portal's advisors.

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