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Statistics

Master of Science (MSc)

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  1. Thesis-based

    Academic background: Details can be found here.

    Course load: STAT 600 and 5 courses which must include STAT 701 and STAT 721. At least 3 courses have to be from List C.

    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: Details can be found here.

    Course load: STAT 600 and eight courses. At least 3 courses have to be from List C. At least 4 courses (not counting STAT 600) have to be at or above the 600 level.

    Thesis or project: Must register and attend STAT 600A in Fall and STAT 600B in Winter and obtain a pass grade.

    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.

  • Rohana Ambagaspitiya: Renewal risk processes, statistics, probability theory and stochastic processes
  • Mina Aminghafari : Statistical/machine learning, Functional data analysis/Time series, Unsupervised learning, Non-parametric statistics
  • Alexandru Badescu: Mathematical finance, actuarial science
  • 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.
  • Qingrun Zhang: Machine learning, High-dimensional data mining, Biostatistics, Bioinformatics

  1. List C courses

    STAT 701 Theory of Probability I 
    STAT 721 Statistical Inference 
    STAT 631 Computational Statistics
    STAT 633 Survival Models
    STAT 635 Generalized Linear Models
    STAT 641 Statistical Learning

  2. Other courses

    STAT 603 Applied Statistics for Nursing Research
    STAT 619 Bayesian Statistics
    STAT 625 Multivariate Analysis
    STAT 633 Survival Models
    STAT 637 Non-linear Regression
    STAT 601.20 Topics in Probability and Statistics (Nonparametric Statistics)
    STAT 601.21 Topics in Probability and Statistics (Advanced Statistical Methods and Applications)
    STAT 601.23 Topics in Probability and Statistics (Asymptotic Statistical Inference)
    STAT 601.24 Topics in Probability and Statistics (Markov Processes)
    STAT 601.25 Topics in Probability and Statistics (Longitudinal Data Analysis)
    STAT 601.27 Topics in Probability and Statistics (Data Mining and Machine Learning with R) 
    STAT 601.28 Topics in Probability and Statistics (Deep Learning and Its Hands-on Practice)
    STAT 601.29: Topics in Probability and Statistics (Infectious Disease Modelling)

    Note: Descriptions of STAT 601 topic courses are available here

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.