Masters in Data Science in Canada for International Students

A masters in data science in Canada is one of the most searched postgraduate choices among international applicants, and for good reason. Canadian universities offer a wide range of formats, from ten-month professional programs to two-year thesis-based degrees, each with different prerequisites, costs and outcomes. This guide compares universities by exact degree title, location, duration, format, tuition, eligibility, English language requirements, GRE or GMAT policy, intake, application deadline and experiential learning options such as co-op placements and capstone projects. Because fees and requirements are revised by universities every year, always cross-check the figures below against the official program page before submitting an application.

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Key Takeaways:

Canadian universities offer professional, coursework-based, and research-focused data science master’s programs. Applicants should compare prerequisites, curriculum, tuition, co-op or capstone options and career alignment before choosing a program. Fees, deadlines, and admission requirements change often and must be verified on the official program page before you apply.world.

What is a Masters in Data Science in Canada?

A masters in data science in Canada is a postgraduate program that combines statistics, programming, machine learning, data management, and analytical problem-solving. Depending on the university, it may be offered under different official titles, such as Master of Data Science (MDS), Master of Science (MSc) in Data Science and Analytics, Master of Science in Big Data, Master of Data Science and Artificial Intelligence (MDSAI), or a related analytics degree such as a Master of Management in Analytics. The exact title, structure, and depth of the curriculum vary by school, so applicants should not assume that every “data science”-sounding program is identical.

Benefits of Pursuing a Master’s in Data Science in Canada

Study abroad in Canada has become a leading destination for a data science masters in Canada because of its combination of academic depth and practical exposure:

• Applied, industry linked curricula. Several programs, such as the professional degrees at UBC and SFU, were built with input from industry advisory panels and are refreshed regularly to reflect current tools and techniques.
• Co-op and capstone options. Many Canadian data science programs build in mandatory or optional co-op work terms, industry capstone projects or internships, giving students real project experience before graduation.
• Research strength. Universities such as the University of Alberta and the University of Waterloo maintain strong research groups in machine learning and statistics, with Waterloo’s data science program recognized by the Vector Institute.
• Access to a growing tech sector. Toronto, Vancouver, Waterloo, Montreal and Ottawa all host active data and AI industry clusters, which supports internship placement and later hiring.
• A pathway to Canadian work experience. Most master’s degree holders from a Designated Learning Institution (DLI) can apply for a Post-Graduation Work Permit (PGWP), subject to institution, program and current Immigration, Refugees and Citizenship Canada (IRCC) requirements, which is covered in more detail later in this guide.

Masters in Data Science in Canada: Key Program Highlights

Note: These are general patterns observed across the programs profiled below. Always verify current duration, format and specialization options on each university’s official program page.

Top Universities for Masters in Data Science in Canada: Fees and Eligibility Compared

1. University of British Columbia (UBC): Master of Data Science (MDS)

The UBC Master of Data Science (MDS) is a 10-month, non-thesis professional degree jointly developed by UBC’s Department of Computer Science and Department of Statistics. It is offered on the Vancouver campus, the Okanagan (Kelowna) campus, and as a separate Master of Data Science in Computational Linguistics (MDSCL) program. The Vancouver program consists of 30 credits delivered through lab oriented, cohort-based courses, many taught in four-week blocks, and is intended primarily for students who do not already hold an undergraduate degree in computer science or statistics, though all applicants are reviewed individually.

Eligibility: Applicants generally need the equivalent of a four-year bachelor’s degree with a minimum B+ average (76 percent, or a 3.30 GPA at UBC) in their final two years of study. No GRE or other standardized admissions test is required. English proficiency is required for applicants whose prior degree was not taught in English. 

Tuition and scholarships: UBC does not publish one single confirmed tuition figure across all its own domains for this program, and third-party admissions guides report a range from roughly CAD 49,000 to over CAD 60,000 for the full 10-month program.

Suitability: UBC MDS suits applicants from a wide range of quantitative undergraduate backgrounds who want an intensive, applied, cohort based entry point into data science, and who are comfortable with a fixed September start and no deferral option.

2. Simon Fraser University (SFU): Master of Science in Big Data

SFU’s School of Computing Science offers the Master of Science in Big Data, a professional, non-thesis program built around lab-based, cohort-style teaching combined with a mandatory co-op placement. The program is designed for students with a strong quantitative background and trains graduates to become data architects, data scientists, data solutions architects, and chief data officers. The curriculum, developed with input from an industry advisory panel, covers scalable algorithms, distributed systems, cloud computing, streaming data, machine learning, data mining, graph analytics, and data visualization.

Program structure: The degree requires a minimum of 30 units of graduate work, split between 15 credits of coursework, 12 credits of specialized lab work, and 3 credits allocated to a co-op work term, regardless of whether the placement runs 4 or 8 months. Students typically complete two 9-credit study terms, one or two co-op terms, and a final study term, for a total program length of 16 to 20 months.

Eligibility: Applicants who completed their degree in Canada need a bachelor’s degree in computer science or a related field with a CGPA of at least 3.00 out of 4.33 (a B average) or a minimum of 3.33 out of 4.33 on their last 60 credits. English proficiency is waived for applicants who completed a prior degree taught in English at a recognized institution in an approved country; all other applicants must meet SFU’s standard graduate English requirements.

Tuition: SFU’s official published rate is CAD 13,856.16 per term for international students and CAD 8,768.40 per term for domestic students. Since the program is completed in four terms, total program tuition is CAD 55,424.64 for international students and CAD 35,073.60 for domestic students. 

Suitability: This program suits applicants who already have a computer science foundation and want mandatory, structured co-op experience built directly into the degree, rather than an optional add-on.

3. University of Waterloo: Master of Data Science and Artificial Intelligence (MDSAI)

The University of Waterloo’s Master of Data Science and Artificial Intelligence (MDSAI) is offered by the Department of Data Science within the Faculty of Mathematics and is recognized by the Vector Institute. QS World University Rankings by Subject ranked the university’s data science and AI offering second in Canada in 2024. The program blends computer science, statistics, combinatorics and optimization, and applied mathematics.

Format options: MDSAI is available as a full-time co-op program, completed in about 16 months across two study terms, a four-month work term, and a final study term, or as a part-time, non-co-op, in-person program designed to be completed within 9 terms (36 months), taking one course per term.

Eligibility: Applicants need an honors bachelor’s degree or equivalent in data science, computer science, statistics, mathematics, or a related field, with a minimum overall average of 78 percent, along with senior-level experience in computer science or statistics. Application materials include a resume, a Supplementary Information Form (SIF), transcripts, and three references.

Tuition: Waterloo directs applicants to its Finance office’s graduate tuition page for the current, program-specific per-term tuition and incidental fees. 

Suitability: Waterloo MDSAI is a strong fit for applicants who already hold a strong quantitative honours degree and want a research adjacent, Vector Institute recognized credential with a structured co-op option.

4. Toronto Metropolitan University (TMU): MSc in Data Science and Analytics

TMU’s MSc in Data Science and Analytics is an interdisciplinary degree that can be completed as a 1-year full-time Major Research Paper (MRP) program, a 2 year part-time MRP program, or a 2 year full-time thesis program. Courses combine lecture-based learning with hands-on lab work on real-world datasets, and the program can be completed fully virtually, fully in person, or through a mix of both.

Prerequisites: Applicants without a directly relevant academic background may need to demonstrate the equivalent of specific foundational courses, documented through prior coursework or through TMU’s own continuing education courses covering statistics and R programming, database management, and Python programming for data science.

Eligibility: Applicants generally need a four-year bachelor’s degree in engineering, science, business, economics, or a related discipline, with a minimum GPA equivalent to 3.00 out of 4.33 (a B average) in the final two years of study, along with demonstrated working knowledge of statistics, data structures, algorithms, databases, and R. 

Funding: TMU notes that graduate funding is typically a combination of teaching assistant contracts, scholarships, awards and stipends rather than automatic full funding. 

Tuition: The exact, current official tuition figure was not confirmed on TMU’s own graduate program page at the time of writing; third-party estimates place the first-year cost in the mid to high twenty thousands in CAD, but this should be verified directly with the university. 

Suitability: TMU suits applicants from a wide range of quantitative undergraduate backgrounds, including business and economics, who want flexibility in study format and pace.

5. Carleton University: Data Science, Analytics and Artificial Intelligence (Master of Computer Science / Master of Engineering)

Carleton offers data science study through concentrations within its Master of Computer Science (MCS) and Master of Engineering (MEng) programs, titled Data Science, Analytics, and Artificial Intelligence, and through the Collaborative Specialization in Data Science (CUIDS), a joint initiative with the University of Ottawa. CUIDS is not a standalone degree; students must first be admitted to and remain registered in one of the participating master’s programs and separately opt in to the data science specialization through their Carleton 360 application.

Eligibility: For the MCS and MEng concentrations, applicants generally need a minimum overall GPA equivalent to a B minus and English proficiency demonstrated through IELTS, CAEL, Duolingo, PTE or an equivalent accepted test, with specific score thresholds published on the official program page. Requirements for the collaborative specialization are set by the participating department to which you apply.

Tuition: The exact, current official tuition figure was not confirmed on a single Carleton domain page at the time of writing; verify the current amount directly with the university before applying.

Suitability: Carleton is a reasonable option for applicants who want to combine a computer science or engineering master’s degree with a structured data science, analytics, and AI concentration, or who are interested in the interdisciplinary CUIDS specialization.

6. University of Alberta: MSc with a Specialization in Statistical Machine Learning / Modelling, Data and Predictions

The University of Alberta offers data science-adjacent study through its Department of Mathematical and Statistical Sciences and Department of Computing Science, including an MSc specialization in Statistical Machine Learning and a related specialization in Modelling, Data, and Predictions. These are thesis-based programs that emphasize the theoretical design and analysis of machine learning algorithms alongside applied statistical modeling and computational methods.

Eligibility: The department’s official minimum admission requirement is a four-year undergraduate degree with a strong background in mathematics, physics or statistics, and a minimum admission GPA of 3.3 on a 4-point scale from the University of Alberta, or an equivalent qualification and standing from a recognized institution. 

Program length: The MSc is designed to be completed in 20 to 24 months, with a maximum permitted completion time of 4 years as set by the Faculty of Graduate and Postdoctoral Studies. 

Tuition: Publicly available figures for this program vary considerably across third-party sources, which suggests the exact current, official rate should be confirmed directly on the University of Alberta’s tuition and fees page before applying

Suitability: This route suits applicants who want a more research-intensive, thesis-based path into data science and machine learning, ideally with an existing strong mathematics or statistics foundation, rather than a short, coursework-only professional degree.

7. McGill University: Master of Management in Analytics (MMA)

McGill’s Desautels Faculty of Management offers the Master of Management in Analytics (MMA), a 45-credit, non-thesis program that blends advanced statistics, artificial intelligence and business strategy. It is not a data science degree in the strict computer science sense, but it is one of the more prominent related analytics degrees for students interested in applying data science to business decision making, and it is included here for that reason.

Format: The in-person MMA can be completed in 12 months, or in 16 months if the student takes the internship option and returns for a fifth term. The online MMA is completed over 24 months and begins in the Winter term. The in-person program begins in the summer term.

Eligibility: The program targets recent graduates and early career professionals with strong quantitative skills, particularly those from engineering, computer science, statistics, economics or business backgrounds, and welcomes applicants who are still completing their final year of a bachelor’s degree at the time of application. Applications require two short essays on the applicant’s goals and two academic or professional referees.

Tuition: McGill’s official tuition page lists CAD 77,600 for the Summer 2026 in-person entering class, and CAD 72,600 for the Winter 2026 online entering class, both before any applicable residency based tuition waiver, which reduces net tuition to CAD 46,530 for students who can demonstrate Canadian citizenship or permanent residency. McGill notes that the MMA program does not offer needs based financial aid, though all admitted students are automatically considered for merit based entrance scholarships.

Intake and financial documentation: Because Quebec sets its own financial requirements for international students under the ministère de l’Immigration, de la Francisation et de l’Intégration (MIFI), applicants must also budget for MIFI’s minimum required funds for tuition, transportation, settlement fees, health insurance and living expenses, which are adjusted annually.

Suitability: MMA suits applicants who want a business focused, one year (or online, two year) route into applied analytics and AI, particularly if they are aiming for management or strategy adjacent analytics roles rather than a purely technical data science role.

How to Choose the Right Data Science Master’s Program in Canada

When comparing a masters in data science in Canada for international students, weigh the following factors together rather than focusing on tuition or ranking alone:

• Curriculum depth versus your background. If you do not have a computer science or statistics undergraduate degree, look for programs explicitly designed for a broader intake, such as UBC’s MDS, rather than programs that assume advanced prior coding and math training.
• Degree format. Decide whether you want a short, intensive professional degree (10 to 12 months), a co-op integrated degree (16 to 20 months), or a longer thesis based research degree (20 to 24 months), based on your career goals and budget.
• Prerequisites. Confirm whether you need to complete bridging courses in statistics, programming or databases before you are considered eligible, as several universities specify exact prerequisite courses.
• Tuition basis. Always confirm whether a published figure is annual, per term, or total program tuition, since these are frequently mixed together in third party guides.
• Experiential learning. If gaining Canadian work experience during your studies matters to you, prioritize programs with mandatory or well established co-op placements, such as SFU’s Big Data program or Waterloo’s MDSAI co-op stream.
• University reputation and location. Consider the local job market for data roles; Toronto, Vancouver, Waterloo, Ottawa and Montreal all have distinct industry strengths.
• Career alignment. If your goal is a technical data science or machine learning engineering role, a computer science or statistics rooted degree is usually more directly relevant than a business analytics degree, and vice versa if your goal is a management or strategy role.

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Career Opportunities for MS in Data Science in Canada

Canada is known for its IT sector. Completing an MS in Data Science in Canada has a good scope of career opportunities. Some of the career opportunities are as follows:

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Conclusion

A masters in data science in Canada can take several different forms, from UBC’s fast, professional MDS to McGill’s business-focused MMA and the University of Alberta’s thesis based Statistical Machine Learning specialization. Each program differs in its exact degree title, prerequisites, format, tuition structure, experiential learning options, and tuition and admission requirements are revised by universities on their own schedules. Before you apply to any MSc in data science in Canada, cross-check the current tuition, eligibility, English requirements, application deadline and PGWP eligibility directly on the university’s official website, and consult IRCC’s website for the latest immigration rules that apply to your specific program and start date.

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