Biostatistics is the study, development, and application of statistical methods and data science in the health sciences.

Biostatisticians may work on diverse topics in health research, including clinical studies (both medical and pharmaceutical), epidemiology and public health, genetics, spacial/geographical data, imaging, machine learning, and health economics. The data encountered by biostatisticians include those produced in randomized controlled trials, observational studies, and natural experiments. New interest is arising in the analysis of the “big data” collected through the usage of provincial health systems in Québec and elsewhere. For more information on the biostatistical research being done at Université de Montréal, see the list of biostatistics professors.

Statistics is a large field that encompasses many areas of application (including economics, engineering, business and industry, psychology, ecology, environment and weather, agriculture, human health, veterinary sciences, etc). Biostatistics is therefore a subdiscipline of statistics. However, because biostatisticians focus on specific questions in health, they are often experts and developers of statistical techniques that are not used by other types of statisticians. Biostatisticians may work with medical professionals, epidemiologists, public health experts, pharmacists, geneticists, basic scientists, and so on. Therefore, they must operate within the culture of these fields. These different cultures, the scientific questions of interest, and the particularities of the data collected all affect the direction of the development of statistical methods. However, biostatisticians enjoy different degrees of specialization, many preferring to work in a number of different subject matter domains!

There are many opportunities for graduates holding a degree in Biostatistics and shortages of skilled analysts across various specializations. Graduates will be able to work as statistical analysts in various organizations in the health system (e.g. The Public Health Agency of Canada, The National Institute of Public Health of Quebec), in research centers of universities or hospitals, for consulting companies, as well as in the pharmaceutical or biotechnology industry. Our graduates have also found exciting employment in data science startups in Montreal and internationally.

• A strong background in statistics. Often, this means having taken undergraduate level mathematics and statistics courses. Experience in computer science and programming is also highly valued. However, different programs and supervisors may have different requirements. If your statistical expertise is weaker, you may be able to take extra classes to increase your knowledge.


• An undergraduate degree (generally one in mathematics, statistics, econometrics, or a related area). Some students successfully transition into biostatistics from different fields by taking extra statistics classes during their undergraduate program or at the beginning of their graduate work. We generally require a master’s degree before pursuing doctoral work.


• An interest in some area of health sciences. Without this, you may find the work particularly boring and difficult!

There are currently several options for someone interested in pursuing graduate studies in biostatistics at Université de Montréal. Biostatistical research is being done across four faculties, each of which can supervise biostatistical graduate students, masters or PhD. However, the degree type and programme requirements vary. The key to pursuing graduate studies/research on a topic that will most interest you is to identify and contact the professor(s) you are interested in working with. It is recommended to contact (email) the professor(s) you’d like to work with before applying.

• Enroll in the Département de mathématiques et statistique (Department of Mathematics and Statistics) and follow the thesis-based masters or PhD programmes in statistics. For a course based (non-thesis) masters in statistics, there is also a biostatistics option, which includes an internship in a research centre. It is recommended to seek a primary or co-supervisor who specializes in biostatistics (note that co-supervisors can be outside of the department). Professor: Janie Coulombe.
• Enroll in the Département de médecine sociale et preventive (Department of social and preventative medicine) and follow the master’s in Epidemiology or PhD in Public Health, Option: Epidemiology with at least one supervisor who is a biostatistician. Professors of Biostatistics: Marie-Pierre Dubé, Benoît Mâsse, Bouchra Nasri, Mireille Schnitzer, Marie-Pierre Sylvestre.
• Enroll in the Département de sciences de la décision (Department of Decision Sciences) and follow the master’s in Science - Data Science and Business Analytics. Professor : Aurélie Labbe.
• Faculty of Pharmacy: register for the Master's degree in Pharmaceutical Sciences: option Medication and Population Health, concentration in Statistics in Pharmaceutical Sciences or PhD with thesis in Pharmaceutical Sciences. Professor: Mireille Schnitzer.

Regardless of your programme enrollment, you may be interested in following graduate courses in statistics or biostatistics. Note that these courses each have their own prerequisites.

• STT 3260 Modèles de survie (Survival modeling)
• STT 6100 Méthodes avancées d'inférence (Advanced statistical inference)
• STT 6115 Théorie de la décision bayésienne (Bayesian decision theory)
• STT 6230 Méthodes non paramétriques avancées (Advanced nonparametric statistics)
• STT 6300 Méthodes asymptotiques (Asymptotic methods)
• STT 6415 Régression (Regression)
• MAT 6717 Probabilités (Probability)
• PHM 7078 Méthodes avancées en inférence causale (Advanced Methods in Causal Inference)
• MSO 6014 Épidémiologie intermédiaire (Intermediate epidemiology)
• MSO 6018 Introduction à l'épidémiologie génétique (Introduction to genetic epidemiology)
• MSO 6028 Introduction aux théories de la mesure (Introduction to the theory of measurement)
• MSO 6075 Études et devis expérimentaux (Experimental studies)
• MSO 6118 Analyse de survie (Survival analysis)
• MSO 6611  Analyses longitudinales et multi-niveaux (Longitudinal and multi-level analysis)
• SPU 7152 Épidémiologie avancée (Advanced epidemiology)