This Master of Science in Epidemiology with Specialization in Biostatistics is is a 12-month full-time program (part-time available on a case-by-case basis). All students are required to complete eight courses in two terms (including 6 core courses and two elective courses) and a one-term supervised practicum project in biostatistics. In the practicum, students will complete either a research project pertaining to some aspect of the biostatistics methodological research work of their supervisor or a practicum placement with an academic or industry-based research group pertaining to the biostatistics applications. When all program requirements are completed satisfactorily, students will be awarded either a MSc in Public Health Sciences, Specializing in Biostatistics (if they are registered in the Department of Public Health Sciences), OR MSc in Mathematics and Statistics with Specialization in Biostatistics (if they are registered in the Department of Mathematics and Statistics).
Completing Your Degree
Students are accepted for a September start date and, if enrolled in full-time studies, are expected to meet the milestones listed below:
Fall
- Introduction to Epidemiology (EPID 801)
- Statistical Inference (STAT 853)
- Computational Data Analysis (STAT 862) or an elective, if taking Linear Regression (EPID 825) and Generalized Linear Models and Survival Anaylsis (EPID 826) in the winter term.
- One Elective
Winter
- Intermediate Epidemiology (EPID 804)
- Advanced Methods in Biostatistics (EPID 823)
- Survival Analysis (STAT 886)
- One Elective if taking Computational Data Analysis (STAT 862) in the fall term, or Linear Regression (EPID 825) and Generalized Linear Models and Survival Anaylsis (EPID 826)
Summer
Courses
The following is a brief overview of course content of the Master of Epidemiology specializing in Biostatistics program, including core courses offered by the Department of Public Health Sciences and Department of Mathematics and Statistics and some elective courses offered by the various departments identified.