What is Biostatistics ?

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

What do Biostatisticians work on?

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.

How is Biostatistics different from Statistics?

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!

What are the employment opportunities with a degree in Biostatistics?

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.

"I think it is much more interesting to live with uncertainty than to live with answers that might be wrong."

Richard Feynman

Studying Biostatistics at Université de Montréal

General Requirements

  • 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!

Program Options

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.

"Statistics is the grammar of science."

Karl Pearson


  • Janie Coulombe

  • Assistant Professor
    Faculty of Arts and Sciences - Department of Mathematics and Statistics
  • Causal inference, informative observations, missing data, longitudinal data.
  • Professionnal
  • Google Scholar
  • Aurélie Labbe

  • Professor
    HEC - Department of Decision Science
  • Genetics, neuroscience, transportation, road safety, Bayesian statistics, analysis of high-dimensional data
  • HEC

  • Bouchra Nasri

  • Assistant Professor
    School of Public Health - Department of Social and Preventive Medicine
  • Dependance modeling, time series, machine learning, infrastructural and public health impacts of climate change
  • Professional
  • ResearchGate
  • Google Scholar
  • Mireille Schnitzer

  • Associate Professor
    Faculty of Pharmacy
    School of Public Health - Department of Social and Preventive Medicine
  • Causal inference, semiparametric efficiency, longitudinal data analysis, meta-analysis
  • Professional
  • Université de Montréal
  • Google Scholar

  • Marie-Pierre Sylvestre

  • Associate Professor
    School of Public Health - Department of Social and Preventive Medicine
  • Trajectory modelling, flexible methods for curve estimation, methods for genome-wide association studies, cumulative exposure modelling, survival analysis, longitudinal models, Monte Carlo studies.
  • Professional
  • Google Scholar
  • Université de Montréal
"In comparing the deaths of one hospital with those of another, any statistics are justly considered absolutely valueless which do not give the ages, the sexes, and the diseases of all the cases."

Florence Nightingale, Notes on Nursing (1860)