Program information
MSc in Biostatistics
The program prepares MSc graduates for positions that require expertise in data management, study design, and statistical modelling.
After completing the program, graduates will be able to:
- Demonstrate proficiency in statistical theory
- Apply statistical theory to problems in the medical, biological, and agricultural sciences
- Collaborate on research teams
- Address problems involved in the collection and management of data
- Participate actively in the drawing of conclusions from data analysis and in the presentation and writing of research reports and papers.
PhD in Biostatistics
The program prepares PhD graduates to assume faculty or other research-oriented positions in academic institutions or to take leadership positions in organizations that conduct health-related research.
After completing the program, graduates will be able to:
- Develop new statistical methods using existing theory or apply existing statistical methods to address problems in the medical, biological, and agricultural sciences
- Collaborate on research teams
- Take a leadership role in study design, data management, statistical analysis, and interpretation of study results
- Be proficient in both the oral and written presentation of research results.
Admission requirements
MSc
- A four-year BSc or BA degree from a recognized university in one of the following disciplines: mathematics, statistics, applied statistics, biostatistics, quantitative psychology, or any other discipline with a strong background in mathematics or statistics
- A cumulative weighted average of at least 75% (U of S grade system equivalent) in the last two years of study (e.g. 60 credit units)
- Proof of English language proficiency may be required for international applicants and for applicants whose first language is not English
PhD
- Thesis-based M.Sc. in a relevant program - mathematics, statistics, applied statistics, biostatistics, quantitative psychology, or another discipline with a strong background in mathematics or statistics
- A cumulative weighted average of at least 75% (U of S grade system equivalent) in the last two years of study (e.g. 60 credit units)
- Proof of English language proficiency may be required for international applicants and for applicants whose first language is not English
To be eligible for admission as a PhD student, an applicant must have taken the following courses (or equivalents) during his/her MSc program: CHEP 800.3 (Epidemiology I), STAT 850.3 (Mathematical Statistics and Inference), and PUBH 842.3 (Current Topics in Biostatistics and Statistical Applications).
Tuition
Thesis or project based program
Graduate students in a thesis or project based program pay tuition three times a year for as long as they are enrolled in their program.
| Term | Canadian | International |
|---|---|---|
| September 1 - December 31, 2016 | $1300.00 | $1950.00 |
| January 1 - April 30, 2017 | $1300.00 | $1950.00 |
| May 1 - August 31, 2017 | $1300.00 | $1950.00 |
| Total per academic year | $3900.00 | $5850.00 |
Tuition information is accurate for the current academic year and does not include student fees. For detailed tuition and fees information, visit the official tuition website.
Click here for information about funding.
Application process
Find a supervisor
Find a potential research supervisor from the list on the College of Graduate Studies and Research program page. Read about the work each supervisor is currently doing and, if you think you'd like to work with them, contact them and describe your research interests and past academic experience. If they are accepting students, they will instruct you to begin a formal application.Submit an online application
Faculty of the Collaborative Program in Biostatistics
The Biostatistics Program is a collaborative venture of the School of Public Health, Department of Community Health and Epidemiology and the Department of Mathematics and Statistics. The core faculty involved in this graduate program are identified below:
| Faculty | Department/College | Areas of Research Interests |
|---|---|---|
| Punam Pahwa Program Director |
Department of Community Health and Epidemiology, College of Medicine | Applications of longitudinal data analysis techniques to various diseases/health conditions (focus on respiratory diseases, cancer, and farm injuries) related to rural occupational and environmental exposures; Analysis of longitudinal complex survey data analysis; Missing data; Analysis of quality of life data |
| Shahed Khan | Department of Mathematics and Statistics, College of Arts and Science | Modeling changepoint data; Longitudinal data analysis; Bayesian inference and Markov Chain Monte Carlo; Survival analysis |
| William Laverty | Department of Mathematics and Statistics, College of Arts and Science | Multivariate statistics; Time series analysis; Experimental and sampling design; Spatial statistics; Computer security |
| Longhai Li | Department of Mathematics and Statistics, College of Arts and Science | Classification and regression with high-dimensional measurements; Detecting differential variables from high-throughput data; Classification and regression with high-order interactions; Modelling DNA sequences |
| Hyun Lim | Department of Community Health and Epidemiology, College of Medicine | Statistical methods for longitudinal data; Recurrent event models in survival analysis; Design and analysis of clinical trials; Epidemiologic studies; HIV/AIDS treatment and prevention studies |
| Juxin Liu | Department of Mathematics and Statistics, College of Arts and Science | Model misspecification; Measurement error and misclassification; Interaction models; Markov Chain Monte Carlo algorithms; Missing data in longitudinal analysis, interplay between Bayesian and frequentist theories |
| Chris Soteros | Department of Mathematics and Statistics, College of Arts and Science | Statistical mechanics; Computer simulation; Combinatorics and bioinformatics |
| Raj Srinivasan | Department of Mathematics and Statistics, College of Arts and Science | Applied probability; Queueing theory; Queueing networks |
| Michael Szafron | Assistant Professor, School of Public Health | Monte Carlo simulations; Statistical Methods associated with Monte Carlo data; Computational biomodelling of the structure/function of biopolymers; Statistical Methods for the health sciences including survey data |
| Cindy Xin Feng | Assistant Professor, School of Public Health | Spatial statistics and disease mapping; Statistical methods for survival and longitudinal data; Environmental and ecological statistics; Infectious disease surveillance; Functional data analysis |
Contact us
For further information on the Biostatistics program, please contact: