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Biostatistics

Master of Science (MSc)

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  1. Thesis-based (Interdisciplinary Specialization offered with Community Health Sciences)

    Academic background: Details can be found here. 

    Course load: BIST 600 and 5 courses which must include Community Health Sciences 640 and STAT 721. At least 1 course has to be from List A and 1 course from List B. The third course can be from either list. Please refer to List A and List B in the "Course lists" expandable section below.

    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: BIST 600 and eight courses that must include BIST 601 and BIST 610. At least 3 courses have to be from List C and at most 3 courses in Biostatistics or Statistics (which may include courses from List C).  Please refer to List C in the "Course lists" expandable section below.

    Thesis or project: Must register and attend BIST 600 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.

List A: Epidemiology and Health Courses

BIST 601 Biostatistical Consulting
MDCH 641 Introduction to Clinical Trials
MDCH 644 Surveillance I: Data Handling for Infection Control
MDCH 647 Clinical Epidemiology
MDCH 649 Epidemiology of Infectious Diseases
MDCH 661 Health Economics I
MDCH 662 Economic Evaluation
MDCH 663 Decision Analysis in Health Economics
MDCH 664 Administrative Data Analysis Methodology
MDCH 681 Health Research Methods
MDCH 740 Advanced Epidemiology
MDCH 741 Systematic Reviews and Meta-analysis

List B: Biostatistics/Statistics Courses

BIST 611 Infectious Disease Modelling
MDCH 611 Models for Health Outcomes
MDCH 612 Models for Repeated Measures Studies and Time-to-Event Studies
STAT 619 Bayesian Statistics
STAT 625 Multivariate Analysis
STAT 631 Computational Statistics
STAT 633 Survival Analysis
STAT 635 Generalized Linear models
STAT 637 Non-linear Regression
STAT 641 Statistical Learning
STAT 701 Theory of Probability I

List C: Statistics 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

Other courses:

BIST 601 Biostatistical Consulting
BIST 610 Fundamentals of Biostatistics and Epidemiology
BIST 611 Infectious Disease Modelling
BIST 620 Research Project in Biostatistics (requires faculty supervisor and suitable project)
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

 

  • Mina Aminghafari: Machine learning, unsupervised learning, functional data analysis, time series forecasting, non-parametric statistics
  • Gemai Chen:  Parametric and non-parametric regression, non-linear time series modelling of environmental changes, extreme-value analysis, goodness of fit, emperical process, sample survey, quality control, high dimensional data analysis, survival analysis, financial & Economic model building and forecasting, statistical consulting
  • Rob Deardon: Bayesian statistics and computation, infectious disease epidemiology and surveillance, statistical learning, spatial epidemiology, experimental design
  • Yunqi Ji:  Longitudinal data analysis, survival analysis, missing data and measurement errors, machine learning, data visualization, dynamic system modelling, administrative database analytics, clinical analytics, health services research, health system performance evaluation
  • Karen Kopciuk: Multi-state models, survival data analysis, multivariate data analysis, statistical genetics, genetic risk, genetic epidemiology
  • Chel Hee Lee: Theory and application of imprecise probabilities, design and analysis of clinical trials, and statistical problems in clinical research and services
  • Alex de Leon: Assessment of diagnostic tests, copula models, estimating functions and estimating equations, statistical problems in medicine, diagnostic tests, pseudo and composite likelihood
  • Quan Long: Machine learning, precision medicine, omics big-data integration, bioinformatics
  • Xuewen Lu: Big data and high-dimensional data analysis, empirical likelihood, survival analysis and randomly censored data, nonparametric and semiparametric models    
  • Hua Shen: Analysis of recurrent events, longitudinal data analysis, general linear models, causal inference, mixture models, statistical learning, survival analysis
  • Jingjing Wu: Minimum distance estimation, non/semi-parametric models, regression/logistic regression models, mixture models, case-control studies, survival analysis, genetic studies
  • Qingrun Zhang: Machine learning, high-dimensional data mining, biostatistics, bioinformatics

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 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).
  • 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.