Our research-intensive statistics program gives you the strong quantitative skills to solve real-world problems with data. Combine your studies with work experience through our co-op option, which has you complete three paid work terms in a variety of professional settings as part of your studies. Through our diverse range of courses, you’ll learn how statistics can be applied to virtually every field, and explore topics from probability, finance and game theory to statistical modeling and analysis of big data.
Our major and minor statistics programs are designed to be combined with other programs, such as health studies, environmental science, public policy, international development studies, economics, psychology and many more, so you can build a degree around your interests and goals. In our specialist program, you’ll take a full set of courses on the theory and methodology of statistics, and select one of three streams, each of which provides immediately useful, job-related skills:
- Statistical Machine Learning and Data Mining stream: This stream may be the only program of its kind (we can’t find any other like it!). You’ll prepare specifically for the high-demand and fast-growing field of statistics and computational sciences. Learn to tackle applied problems in science and technology, and work with the large-scale, high-dimensional and heterogeneous data streams becoming increasingly common in modern industries.
- Quantitative Finance stream: Gain an in-depth theoretical understanding of the core concepts of quantitative finance. This stream is different than similar offerings at U of T and other institutions — it focuses more intensely on the mathematical, statistical and computer science background that underlies concepts of quantitative finance.
- Statistical Science stream: Develop an expertise in proper data collection and analysis, along with a strong understanding of the statistical methodology theories. Graduate well-prepared for employment as a statistician or further graduate studies in statistics.