Master of Data Science
Become an expert in data analytics, problem-solving and advanced computing.
The University of Guelph launched a Master of Data Science program in response to the growing and anticipated need for top-tier data scientists in Canada. You can expect to gain a diverse and comprehensive understanding of data mining; data warehousing and database management; extraction, transformation, and loading (ETL); machine learning; artificial intelligence (AI); statistical modelling; scripting; and data visualization.
You'll build your skills through practical experience via written assignments, oral presentations, case studies, applied course projects and group work. You'll also have the opportunity to develop a portfolio of your work from the program, a valuable showcase in demand by tech employers.
There are ample networking opportunities available to you through the program’s affiliation with the Vector Institute, and connection with U of G's Centre for Advancing Responsible and Ethical Artificial Intelligence (CARE-AI).
Read our Frequently Asked Questions for more information about the program and admissions.
Reasons to Choose the MDS
As a graduate, you will:
- Be poised to fill one of 305,000 digital talent job openings in Canada.
- Enter a field in which Canada invests billions of dollars each year.
- Have the skills to build a career in one of North America's top 15 digital jobs.
Unique Data Science Skills
Our data science master's is unlike any other program in Canada. It is the only data science program with a focus on spatial-temporal data, providing students with unique skills required to handle, manipulate and visualize data collected across these dimensions. Our focus is to prepare you for direct entry to a successful career in data science that directly targets industry needs.
Embedded Ethical Practice
You will learn about issues surrounding the ethical practice of data science and identification of potential biases. This ethics-first approach will allow you to apply data science practice through an ethical lens, and give you a competitive advantage in securing top jobs.
Vector Institute Affiliation
The Master of Data Science is a Vector Institute affiliated program. Students become part of the Vector Institute’s community of renowned researchers, major Canadian companies, and AI startups solving high-impact problems. Benefits of Vector affiliation include:
- Students enrolled in the Master of Data Science are eligible to apply for Vector Scholarships in Artificial Intelligence, valued at $17,500 each.
- Vector-affiliated students have access to career development and networking activities. These opportunities enable students to explore career paths in AI and meet with potential employers.
- Discover career opportunities in Vector's extensive industry network through the Digital Talent Hub, an exclusive online platform that curates AI-related job openings among top Canadian employers.
Information about nomination for scholarships can be found on the Vector Institute website.
Students We Attract
The need for qualified, well-trained data science professionals spans virtually every industry. Our students are top domestic and international students from undergraduate programs that include: Biostatistics; Business; Computer Sciences; Computer, Electrical, Electronic, Environmental, Information Technology, Software, and Systems Engineering; Finance; Mathematics and Statistics; and Physics.
The program's faculty have expertise spanning across U of G with exceptional skills in areas related to data science:
- Ayesha Ali, Associate Professor, Mathematics and Statistics, Bioinformatics Graduate Program
- Daniel Ashlock, Professor, Mathematics and Statistics, Bioinformatics Graduate Program
- Neil Bruce, Associate Professor, School of Computer Science
- Monica Cojocaru, Professor, Mathematics and Statistics
- Rozita Dara, Associate Professor, School of Computer Science
- Lorna Deeth, Assistant Professor, Mathematics and Statistics
- Ali Dehghantanha, Associate Professor, School of Computer Science
- Hermann Eberl, Professor, Mathematics and Statistics
- Zeny Feng, Associate Professor, Mathematics and Statistics
- Daniel Gillis, Associate Professor, School of Computer Science
- Andrew Hamilton-Wright, Associate Professor, School of Computer Science, Bioinformatics Graduate Program
- Julie Horrocks, Professor, Mathematics and Statistics, Bioinformatics Graduate Program
- David Kribs, Professor, Mathematics and Statistics
- Anna Lawniczak, Professor, Mathematics and Statistics
- Xiaodong Lin, Associate Professor, School of Computer Science
- Khurram Nadeem, Assistant Professor, Mathematics and Statistics, Bioinformatics Graduate Program
- Mihai Nica, Assistant Professor, Mathematics and Statistics
- Stacey Scott, Associate Professor, School of Computer Science
- Fei Song, Associate Professor, School of Computer Science
- Fangju Wang, Associate Professor, School of Computer Science
- Yang Xiang, Professor, School of Computer Science
- Honour’s Bachelor’s degree or equivalent from an accredited institution with a minimum overall average of 70% (B-) in the last four semesters of study with: 1) a major or minor in data science, computer science, mathematics, statistics, or a related field; or 2) working knowledge of statistics and computer programming, as demonstrated through completion of university or college level degree credit courses equivalent to the following U of G courses:
- STAT*3240 Applied Regression; and
- CIS*2500 Intermediate Programming.
Please note: prospective students with an Honour’s Bachelor’s degree in an unrelated field who do not meet the above requirements may gain entry to the program after completing the Diploma in Applied Statistics (or equivalent) with a minimum overall average of at least 70% (B-).
Successful applicants must also meet U of G's English Proficiency requirements for admission. If an applicant’s first language is not English, an English Language Proficiency test will be required.
International applicants: please use this guide to compare your academic credentials and determine the grade equivalency needed.
All applications will be received and reviewed by the Data Science Program Committee. The Department encourages applications from members of equity-seeking groups, including those who self-identify as women, visible minorities or Indigenous peoples.
The Department welcomes applications from international students. In addition to the above admission requirements, please see the below helpful resources for applying at the University of Guelph as an international graduate student:
- General information for international applicants
- COVID-19 updates pertaining to U of G graduate students
Program Director and Graduate Program Coordinator:
Dr. Ayesha Ali, Associate Professor,
Mathematics and Statistics, Bioinformatics Graduate Program
Graduate Program Assistant:
Frequently Asked Questions and Program Information
Program information, including completion time and course offerings, can be found below. Information about program admissions, the fall semester, and the program in general can be found on our Frequently Asked Questions page.
- Master of Data Science (MDS)
- Number of semesters to complete
- On campus
- Course-based master's program
- Flipped-classroom mode combining remote and in-person learning.
- Elective courses to choose from
|1||Introduction to Data Science||DATA*6100||0.50|
|1||Data Manipulation and Visualization||DATA*6200||0.50|
|1||Analysis of Big Data||DATA*6300||0.50|
|1||Machine Learning for Sequential Data Processing||DATA*6400||0.50|
|1||Analysis of Spatial-Temporal Data (capstone)*||DATA*6500||0.50|
|1||Applications of Data Science (capstone)*||DATA*6600||0.50|
The program requires the successful completion of 4.00 graduate credits consisting of: six core courses (6 x 0.50 = 3.00) or four core courses (4 x 0.50 = 2.00) and a major research project (1.00); and two elective courses (2 x 0.50 = 1.00).
This table represents a selection of core data science courses students will take through the program, in addition to the required two electives. A full list of courses, including electives, can be found here.
*A core course, but students may elect to complete DATA*6700 Data Science Project, which could take the form of a faculty-supervised research project or work internship, in place of two capstone courses. This option will depend on approval of the Program Director and on the availability of a faculty member who is willing to supervise the major research project.
- Business intelligence developer
- Clinical data manager
- Data administrator
- Data architect
- Data scientist
- Machine learning researcher
- Marketing analyst
- Software engineer