Statistics

Master of Science (MSc)

  1. Thesis-based

    Academic background: Bachelor's degree in statistics or an undergraduate degree in a field related to statistics.

    Course load: STAT 600 and 5 courses which must include STAT 701 and STAT 721. At least 3 courses (not counting STAT 600) have to be at or above the 600 level.

    Thesis or project: A thesis has to be written and defended orally in front of an exam committee.

    Completion time: Two years. If the program is completed in one year, the five required courses will be reduced to four. The maximum time allowed is four years.

    Is part-time available: Yes. The maximum time allowed is six years.

    Course performance level: Should maintain a minimum cumulative GPA of 3.00 calculated on a four-point scale at the end of each registration year and attain at least a B- on each course taken for credit.

    Funding: Full-time thesis-based students will be funded for up to two years or sponsored. Part-time students are not funded.

  2. Course-based

    Academic background: Bachelor's degree in statistics or an undergraduate degree in a field related to statistics.

    Course load: STAT 600 and eight courses which must include STAT 701 and STAT 721. At least four courses (not counting STAT 600) have to be at or above the 600 level.

    Thesis or project: A final project with a corresponding written report has to be submitted to and passed by the supervisor.

    Completion time: 1-2 years. The maximum time allowed is six years.

    Is part-time available: Yes. The maximum time allowed is six years.

    Course performance level: Should maintain a minimum cumulative GPA of 3.00 calculated on a four-point scale at the end of each registration year and attain at least a B- on each course taken for credit.

    Funding: Unfunded.

Successful applicants typically have a bachelor’s degree in statistics or closely related field. For admission to the master's program, the graduate studies committee desire the following courses:

  • Mathematics courses: Calculus, linear algebra
  • Probability course: Introduction to probability
  • Statistics courses: Introduction to statistics, mathematical statistics, linear regression, sampling and experimental design
  • Computing skills: R or equivalent

The above requirements are in addition to the minimum admission requirements of the Faculty of Graduate Studies. Please note that meeting the admission requirements does not guarantee admission. Undergraduate research assistant experience or work experience in a statistics related field is an asset.

  • Rohana Ambagaspitiya: Renewal risk processes, statistics, probability theory and stochastic processes
  • Alexandru Badescu: Mathematical finance, actuarial science
  • Thierry Chekouo: Bayesian statistics and computation; Markov chain Monte Carlo and related simulation methods; functional data analysis; high-dimensional data analysis, bioinformatics, biostatistics
  • Gemai Chen: Parametric and non-parametric regression, non-linear time series modelling of environmental changes, extreme-value analysis, etc.
  • Rob Deardon: Bayesian statistics and computation, infectious disease epidemiology, statistical learning, etc.
  • Alex de Leon: Assessment of diagnostic tests, copula models, estimating functions and estimating equations, statistical problems in medicine, etc.
  • Xuewen Lu: Big-data and high-dimensional data analysis, biostatistics, empirical likelihood, survival analysis and randomly censored data, etc.
  • David Scollnik: Actuarial science, Bayesian statistics and computation, Markov chain Monte Carlo and related simulation methods, mathematical finance
  • Deniz Sezer: Credit risk and finance, super-processes, Markov chain Mote Carlo methods
  • Hua Shen: Biostatistics, analysis of recurrent events, longitudinal data analysis, general linear models, etc.        
  • Anatoliy Swishchuk: Financial mathematics, biomathematics, random evolutions and their applications, stochastic calculus
  • Jingjing Wu: Minimum distance estimation, non/semi-parametric models, regression/logistic regression models, biostatistics, etc.

STAT 619 Bayesian Statistics 
STAT 625 Multivariate Analysis 
STAT 631 Computational Statistics 
STAT 633 Survival Models 
STAT 635 Generalized Linear Models 
STAT 637 Non-linear Regression 
STAT 641 Statistical Learning 
STAT 701 Theory of Probability I 
STAT 703 Theory of Probability II 
STAT 721 Statistical Inference 
STAT 723 Theory of Hypothesis Testing

A master’s thesis-based student must complete a thesis on a topic to be agreed to by the student and their supervisor.

  • After completion of the thesis, the student must pass a thesis oral examination.
  • A master's thesis oral exam committee contains a supervisor, a co-supervisor (if it is applicable), an examiner (an additional member of the University of Calgary academic staff), and an internal examiner (a member of the University of Calgary academic staff whom programs may require to be external to the program).
  • The exam must be scheduled at least four weeks prior to date of oral exam.
  • Examiners must have a copy of the thesis at least three weeks prior to the date of oral exam.
  • Final thesis oral examinations are open.

More information can be found on the Faculty of Graduate Studies website under examinations.