
Thesisbased
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 parttime available: Yes. The maximum time allowed is six years.
Course performance level: Should maintain a minimum cumulative GPA of 3.00 calculated on a fourpoint scale at the end of each registration year and attain at least a B on each course taken for credit.
Funding: Fulltime thesisbased students will be funded for up to two years or sponsored. Parttime students are not funded.

Coursebased
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: 12 years. The maximum time allowed is six years.
Is parttime available: Yes. The maximum time allowed is six years.
Course performance level: Should maintain a minimum cumulative GPA of 3.00 calculated on a fourpoint 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; highdimensional data analysis, bioinformatics, biostatistics
 Gemai Chen: Parametric and nonparametric regression, nonlinear time series modelling of environmental changes, extremevalue 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: Bigdata and highdimensional 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, superprocesses, 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/semiparametric 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 Nonlinear 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 thesisbased 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 cosupervisor (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.