Data Science and Analytics (MSc)
- 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
- Statement of interest
- ¸é±ð²õ³Ü³¾Ã©/°ä³Õ
- Transcripts
- Two letters of recommendation
- English language proficiency requirement
More information on admission requirements. Due to the competitive nature of our programs, it is not possible to offer admission to everyone who applies that meets the minimum entrance requirements for the program.
Prospective students are expected to demonstrate working knowledge of statistics, data structures and algorithms, databases and R software packages. These prerequisites can be documented through prior completion of university or college level degree credit courses equivalent to the following courses that are offered at the G. Raymond Chang School of Continuing Education at ³ÉÈË´óƬ:
- : Data Analytics: Basic Methods (formerly CKME 132: Statistics and R Programming)
- : Data Organization for Data Analysts (formerly CKCS 110: Database Management)
Python Programming for Data Science
( Python Programming for Data Science replaces Data Structures from the Fall 2020 term. The program will keep accepting CCPS305 as a prerequisite course for all those who complete it up to and including the Winter 2021 term)
Students are encouraged to submit applications prior to the first consideration date to increase their chances of securing financial support for their graduate studies. Applications received after the first consideration date will be accepted and reviewed based on spaces remaining in the program.
See application dates.
For detailed graduate tuition and fees information please visit Fees by Program.
For information on scholarships, awards and financing your graduate studies visit Financing Your Studies.
- Domain knowledge
- Machine learning
- Math
- Operations research
- Programming
- Statistics
- Advanced Data Visualization
- Bayesian Statistics and Machine Learning
- Data Mining and Prescriptive Analysis
- Designs of Algorithms and Programming for Massive Data
- Interactive Learning in Decision Processes
- Machine Learning
- Management of Big Data and Big Data Tools
- NLP (Text Mining)
- Social Media Analytics
- Students can apply to participate in DMZ programming, where they have the opportunity to create their own startups or work with companies engaged in big data and data science.
- Students may have direct access to various international partners and/or exchange programs to enhance their learning experience.
- ³ÉÈË´óƬ’s Big Data Initiative (BDI) spans many academic units and research areas. The Data Science Laboratory, RC4 High Performance Computing Facility, and Privacy and Big Data Institute are part of ³ÉÈË´óƬ's cross-university BDI agenda to develop new tools and apply them to advance organizational performance across sectors.
- The program engages with various industry and government partners from diverse domains.
Graduate Admissions
Admissions information and how to apply
Graduate Studies Admissions Office
11th Floor, 1 Dundas Street West
Toronto, ON
Telephone: 416-979-5150
Email: gradhelp@torontomu.ca
For information specific to programs, please see the program contact information below.
Program Contacts
Dr. Ayse Bener
Graduate Program Director
PhD, Information Systems, London School of Economics
Research areas: machine learning, recommender systems and big data applications
Telephone: 416-979-5000 ext. 553155
Email: ayse.bener@torontomu.ca
Igor Rosic
Graduate Program Administrator
Telephone: 416-979-5000 ext. 553693
Email: datascigrad@torontomu.ca