Toronto Metropolitan University M.Sc Data Science and Analytics Fees, Eligibility, Admission & Deadlines

This unique Master of Science (MSc) degree program enables students to develop interdisciplinary skills, and gain a deep understanding of technical and applied knowledge in data science and analytics. Graduates are highly trained, qualified data scientists who can go on to pursue careers in industry, government or research. The program is offered as a one year full-time or two year part-time MRP option and two year full-time Thesis option.

The MSc in Data Science and Analytics is delivered in both lecture-based and hands-on lab learning environments where students can develop and apply their skills to complex, real-world datasets and data science and analytics problems.

Trent is one of the first universities in Canada to work to fill the training gap for professionals in data science. You will be uniquely prepared with the tools and techniques you require to work with and analyze today’s increasingly complex data sets in all areas of the sciences, as well as the business world. This professional program can be completed in 16 months and emphasizes practical skills in visualization, data mining, cloud-based approaches and parallel programming skills. Prepare for careers including Chief Data Officer, Data Scientist, Data Solutions Architect, Business Manager and Business Analyst.

• Completion of a four-year undergraduate (or equivalent degree) from an accredited institution in engineering, science, business, economics or a related discipline
• Minimum grade point average (GPA) or equivalent of 3.00/4.33 (B) in the last two years of study
• English language scores

• B.A. or B.Sc. honours degree (a four year undergraduate bachelor's degree) in one of the traditional disciplines: Anthropology, Biology, Business Administration, Chemistry, Computing & Information Systems, Economics, Geography, Humanities, Mathematics, Physics & Astronomy, Philosophy, or Psychology
• Minimum B+ (77%) or equivalent in the work of the last four semesters or the last two undergraduate years (last ten full credits)
• A university course in differential and integral calculus, and one in probability and statistics or the equivalent
• Some familiarity with linear algebra, and be capable of programming at an elementary level in at least one computational language
• A course in either differential equations or advanced statistics is required, depending on whether the student’s area of research will be mathematics or statistics based