The master of science (MSc) in statistics program offers exposure to a wide range of statistical techniques and provides experience in the application of statistical methods. It teaches statistical expertise for careers in either theoretical or applied statistics.
The MSc program in statistics combines applied and theoretical training in state of the art statistical methodology, hands-on consulting experiences, a project in data analysis or in the development of new statistical methodology, and the opportunity to gain work experience through co-operative education. The program prepares graduates for careers as statisticians in industry, government, consulting, and research organizations. In addition, graduates receive the foundational training to continue on to PhD studies.
Applicants must satisfy the University admission requirements as stated in Graduate General Regulations 1.3 in the SFU Calendar. Applicants with degrees in areas other than statistics are encouraged to apply provided they have some formal training in statistical theory and practice.
This program consists of required courses and a project for a minimum of 30 units. Students with backgrounds in other disciplines, or with an inadequate background in statistics, may be required to complete two undergraduate courses in addition to the program requirements for a minimum of 36 units. The undergraduate courses will not be included in the program cumulative grade point average (CGPA).
Students complete all of*
This course is designed to give students some practical experience as a statistical consultant through classroom discussion of issues in consulting and participation in the department's Statistical Consulting Service under the direction of faculty members or the director.
Students will participate in the department's Statistical Consulting Service under the direction of faculty members or the director.
The statistical theory that supports modern statistical methodologies. Distribution theory, methods for construction of tests, estimators, and confidence intervals with special attention to likelihood and Bayesian methods. Properties of the procedures including large sample theory will be considered. Consistency and asymptotic normality for maximum likelihood and related methods (e.g., estimating equations, quasi-likelihood), as well as hypothesis testing and p-values. Additional topics may include: nonparametric models, the bootstrap, causal inference, and simulation. Prerequisite: STAT 450 or permission of the instructor. Students with credit for STAT 801 may not take this course for further credit.
A modern approach to normal theory for general linear models including models with random effects and "messy" data. Topics include experimental units, blocking, theory of quadratic forms, linear contrasts, analysis of covariance, heterogeneous variances, factorial treatment structures, means comparisons, missing data, multi-unit designs, pseudoreplication, repeated measures mixed model formulation and estimation and inference. Prerequisite: STAT 350 or equivalent.
The theory and application of statistical methodology for analyzing non-normal responses. Special emphasis on contingency tables, logistic regression, and log-linear models. Other topics can include mixed-effects models and models for overdispersed data. Prerequisite: STAT 830 and STAT 850 or permission of instructor.
Tu, Th 9:30 AM – 11:20 AM
AQ 5007, Burnaby
An advanced treatment of modern methods of multivariate statistics and non-parametric regression. Topics may include: (1) dimension reduction techniques such as principal component analysis, multidimensional scaling and related extensions; (2) classification and clustering methods; (3) modern regression techniques such as generalized additive models, Gaussian process regression and splines. Prerequisite: STAT 830 and STAT 853 or permission of instructor.
An introduction to computational methods in applied statistics. Topics can include: the bootstrap, Markov Chain Monte Carlo, EM algorithm, as well as optimization and matrix decompositions. Statistical applications will include frequentist and Bayesian model estimation, as well as inference for complex models. The theoretical motivation and application of computational methods will be addressed. Prerequisite: STAT 830 or equivalent or permission of instructor.
Mo, We 10:30 AM – 12:20 PM
AQ 5007, Burnaby
*Other courses may be substituted for these courses with senior supervisor and graduate program chair approval.
and a project
Students are required to submit and successfully defend a project based on a statistical analysis problem or on the development of new statistical methodology. The project is examined as a thesis and must be submitted to the library.
All students are required to submit and successfully defend a project based on a statistical analysis problem or on the development of new statistical methodology. The project is examined as a thesis and must be submitted to the library. See the Graduate General Regulations Section 1.10 and 1.11 for further information.
Students are expected to complete the program requirements in four to five terms.
Diploma in Financial Engineering
The Diploma in Financial Engineering is designed for students in the MSc program who would like to develop applied skills in the field of finance.
Academic Requirements within the Graduate General Regulations
All graduate students must satisfy the academic requirements that are specified in the Graduate General Regulations, as well as the specific requirements for the program in which they are enrolled.