Master of Science in Statistics (MSc)
Established in 1983, the Department of Statistics at UBC is internationally renowned for its excellence in research and the high calibre of its faculty members. Our programs offers students different options for pursuing their interests and professional goals. We currently have programs at both the Masters and PhD levels. At the MSc level we offer a MSc in Statistics and a MSc in Statistics with a Biostatistics option (in collaboration with the School of Population and Public Health). Students can choose to do a thesis, a final project or an 8-month full-time Co-op placement (internship) outside the Department.
With appropriately selected courses during their MSc program, our students can prepare themselves for a successful career in biostatistics, bioinformatics / genomics, data mining, statistical computing, among others.
What makes the program unique?
The Department is renowned in Canada for its research excellence and its leadership in the research community. Students are engaged through both courses and research, and develop a strong set of skills, both applied and theoretical. The Department has always valued data driven research, consulting and collaboration, and has long held communication and computing skills as crucial for success. These values are apparent not only in individual faculty’s research programs but also in our undergraduate and graduate curriculum.
Graduate students participate actively in our research, teaching and consulting activities, and enjoy a wide variety of opportunities for interaction with other researchers and students on- and off-campus. These opportunities are facilitated by the involvement of our faculty members in various collaborative and interdisciplinary research projects. In addition, our graduate students run their own statistical consulting service, which provides them with professional (paid) experience even before they finish their program.
Contact the program
Meet a Representative
UBC Grad School Info SessionDate: Thursday, 05 August 2021
Time: 17:00 to 18:00
In this session we’ll provide a high-level overview of graduate study, graduate school at UBC, and the application process. This is not a program specific event. The session will cover:
- Why graduate study? – advice on what to consider if you are considering graduate school.
- Differences between undergraduate and graduate study.
- Explanation of the different types of graduate programs at UBC.
- What makes UBC a great place to study at the graduate level.
- How to search UBC’s over 300 different graduate program options.
- Overview of the graduate school application process.
- Next steps on learning more and beginning a grad school application
Who is this webinar for?
This webinar is for anyone who is thinking about studying at the graduate level. It’s for those who’d like to learn more about UBC and gain insight into what it’s like to study at UBC. This webinar is also helpful for anyone who wants to learn more about what is involved in a graduate school application.
Admission Information & Requirements
1) Check Eligibility
Minimum Academic Requirements
The Faculty of Graduate and Postdoctoral Studies establishes the minimum admission requirements common to all applicants, usually a minimum overall average in the B+ range (76% at UBC). The graduate program that you are applying to may have additional requirements. Please review the specific requirements for applicants with credentials from institutions in:
Each program may set higher academic minimum requirements. Please review the program website carefully to understand the program requirements. Meeting the minimum requirements does not guarantee admission as it is a competitive process.
English Language Test
Applicants from a university outside Canada in which English is not the primary language of instruction must provide results of an English language proficiency examination as part of their application. Tests must have been taken within the last 24 months at the time of submission of your application.
Minimum requirements for the two most common English language proficiency tests to apply to this program are listed below:
TOEFL: Test of English as a Foreign Language - internet-based
Overall score requirement: 100
IELTS: International English Language Testing System
Overall score requirement: 7.5
Other Test Scores
Some programs require additional test scores such as the Graduate Record Examination (GRE) or the Graduate Management Test (GMAT). The requirements for this program are:
The GRE is not required.
Prior degree, course and other requirements
The department's Admissions Committee has outlined the following requirements, along with other desired courses and skills. Note that meeting the minimum requirements does not guarantee admission. Note also that an applicant missing a course or two but with other strong attributes, such as work experience as a statistician, will be seriously considered.
required: calculus (differentiation, integration, multivariable calculus) as obtained in UBC MATH 100, 200, 253
required: linear algebra, as obtained in UBC MATH 221
In addition, the student should have some ability in constructing proofs, for instance concerning continuity and limits, such as acquired in UBC MATH 200.
required: an intoductory course including axioms of probability, various common distributions, multivariate distributions and some limit theorems, as obtained through UBC MATH/STAT 302 or via the text Introduction to Probability and Its Applications, 2nd edition, by R.L. Scheaffer.
recommended but not required: stochastic processes, as obtained through UBC MATH 303
required: familiarity with R
recommended but not required: experience in coding such as C/C+ or Python
required: an introductory course in statistical methods, such as UBC STAT 200 or via the text Statistics Data and Models, Pearson Canada, 2015, by De Veaux Velleman and Bock
required: a course in statistical inference/mathematical statistics: theory of estimation and hypothesis testing, such as UBC STAT 305 or via the text Mathematical Statistics and Data Analysis, Wadsworth & Brooks/Cole, 1988, by John Rice
required: either a course in regression analysis or a course in the design of experiments/ANOVA. The regression course should cover the material in UBC STAT 306, with text Applied Linear Regression, Wiley, by S. Weisberg. The design/ANOVA course should cover the material in UBC STAT 404, with text Design and Analysis of Experiments, Wiley, latest edition, by D. Montgomery.
We require a statement of interest (1 page maximum), as well as a CV.
2) Meet Deadlines
September 2022 Intake
Application Open Date04 October 2021
3) Prepare Application
All applicants have to submit transcripts from all past post-secondary study. Document submission requirements depend on whether your institution of study is within Canada or outside of Canada.
Letters of Reference
A minimum of three references are required for application to graduate programs at UBC. References should be requested from individuals who are prepared to provide a report on your academic ability and qualifications.
Statement of Interest
Many programs require a statement of interest, sometimes called a "statement of intent", "description of research interests" or something similar.
Students in research-based programs usually require a faculty member to function as their supervisor. Please follow the instructions provided by each program whether applicants should contact faculty members.
Instructions regarding supervisor contact for Master of Science in Statistics (MSc)
Permanent Residents of Canada must provide a clear photocopy of both sides of the Permanent Resident card.
4) Apply Online
All applicants must complete an online application form and pay the application fee to be considered for admission to UBC.
Faculty and Graduate Students are engaged in a wide variety of cutting edge research areas, and the Statistics Department is a hub of collaborative research with other Departments on campus. Research areas include Bayesian Statistics, Bioinformatics & Genomics, Biostatistics, Environmental & Spatial Statistics, Forest Products Stochastics Modeling, Multivariate & Time Series Analysis, Robust Statistics, and Statistical Learning.
Besides required courses, MSc. students must complete either a thesis or major project. During the first year, students can apply for a Co-op option, and complete two Co-op terms.
Tuition & Financial Support
|Fees||Canadian Citizen / Permanent Resident / Refugee / Diplomat||International|
|Installments per year||3||3|
|Tuition per installment||$1,732.53||$3,043.77|
|Tuition per year|
(plus annual increase, usually 2%-5%)
|Int. Tuition Award (ITA) per year (if eligible)||$3,200.00 (-)|
|Other Fees and Costs|
|Student Fees (yearly)||$1,052.34 (approx.)|
|Costs of living (yearly)||starting at $17,126.20 (check cost calculator)|
All fees for the year are subject to adjustment and UBC reserves the right to change any fees without notice at any time, including tuition and student fees. Tuition fees are reviewed annually by the UBC Board of Governors. In recent years, tuition increases have been 2% for continuing domestic students and between 2% and 5% for continuing international students. New students may see higher increases in tuition. Admitted students who defer their admission are subject to the potentially higher tuition fees for incoming students effective at the later program start date. In case of a discrepancy between this webpage and the UBC Calendar, the UBC Calendar entry will be held to be correct.
Applicants to UBC have access to a variety of funding options, including merit-based (i.e. based on your academic performance) and need-based (i.e. based on your financial situation) opportunities.
Program Funding Packages
MSc. students receive approximately $18,000 per year in funding in the form of Teaching and Research Assistantships. Additional funding opportunities may be available through additional appointments and employment with our student consulting service.
Scholarships & awards (merit-based funding)
All applicants are encouraged to review the awards listing to identify potential opportunities to fund their graduate education. The database lists merit-based scholarships and awards and allows for filtering by various criteria, such as domestic vs. international or degree level.
Teaching Assistantships (GTA)
Graduate programs may have Teaching Assistantships available for registered full-time graduate students. Full teaching assistantships involve 12 hours work per week in preparation, lecturing, or laboratory instruction although many graduate programs offer partial TA appointments at less than 12 hours per week. Teaching assistantship rates are set by collective bargaining between the University and the Teaching Assistants' Union.
Research Assistantships (GRA)
Many professors are able to provide Research Assistantships (GRA) from their research grants to support full-time graduate students studying under their direction. The duties usually constitute part of the student's graduate degree requirements. A Graduate Research Assistantship is a form of financial support for a period of graduate study and is, therefore, not covered by a collective agreement. Unlike other forms of fellowship support for graduate students, the amount of a GRA is neither fixed nor subject to a university-wide formula. The stipend amounts vary widely, and are dependent on the field of study and the type of research grant from which the assistantship is being funded. Some research projects also require targeted research assistance and thus hire graduate students on an hourly basis.
Financial aid (need-based funding)
Canadian and US applicants may qualify for governmental loans to finance their studies. Please review eligibility and types of loans.
All students may be able to access private sector or bank loans.
Foreign government scholarships
Many foreign governments provide support to their citizens in pursuing education abroad. International applicants should check the various governmental resources in their home country, such as the Department of Education, for available scholarships.
Working while studying
The possibility to pursue work to supplement income may depend on the demands the program has on students. It should be carefully weighed if work leads to prolonged program durations or whether work placements can be meaningfully embedded into a program.
Tax credits and RRSP withdrawals
Canadian residents with RRSP accounts may be able to use the Lifelong Learning Plan (LLP) which allows students to withdraw amounts from their registered retirement savings plan (RRSPs) to finance full-time training or education for themselves or their partner.
Please review Filing taxes in Canada on the student services website for more information.
Applicants have access to the cost calculator to develop a financial plan that takes into account various income sources and expenses.
Enrolment, Duration & Other Stats
These statistics show data for the Master of Science in Statistics (MSc). Data are separated for each degree program combination. You may view data for other degree options in the respective program profile.
Completion Rates & Times
This list shows faculty members with full supervisory privileges who are affiliated with this program. It is not a comprehensive list of all potential supervisors as faculty from other programs or faculty members without full supervisory privileges can request approvals to supervise graduate students in this program.
Auger-Methe, Marie (Fisheries sciences; Statistics; Zoology; Animal movement; Polar ecology; Statistical Ecology)
Bloem-Reddy, Benjamin (developing methods for evolving networks whose history is unobserved; distributional limits of preferential attachment networks; uses of symmetry in statistics, computation, and machine learning)
Bouchard-Cote, Alexandre (machine/statistical learning; mathematical side of the subject as well as in applications in linguistics and biology)
Campbell, Trevor (automated, scalable Bayesian inference algorithms; Bayesian nonparametrics; streaming data; Bayesian theory; Probabilistic Inference; computational statistics; large-scale data)
Chen, Jiahua (Statistics; asymptotic theory; empirical likelihood; finite mixture model; sample survey)
Cohen Freue, Gabriela (statistical genomics (focus in proteomics), robust estimation and inference, linear models with endogeneity )
Gustafson, Paul (Statistics; meta-analysis; Parametric and Non-Parametric Inference; Theoretical Statistics; Pharmacoepidemiology; Bayesian statistical methods; Biostatistics and Epidemiology; Causal inference; Evidence synthesis; Partial Identification)
Heckman, Nancy (Statistics; Statistics and Probabilities; functional data analysis; smoothing; splines)
Joe, Harry Sue Wah (Statistics; Statistics and Probabilities; copula construction; dependence modelling; extreme value inference; non-normal time series; parsimonous high-dimensional dependence)
Korthauer, Keegan (Bioinformatics; Genomics; Statistics; Epigenomics; Single-cell analysis; Statistical genomics)
McDonald, Daniel (High dimensional data analysis; Computational methods in statistics; Statistical theory and modeling; Machine learning; Estimation and quantification of prediction risk; Evaluating the predictive abilities of complex dependent data; Application of statistical learning techniques to time series prediction problems; Investigations of cross-validation and the bootstrap for risk estimation)
Nolde, Natalia (Statistics; Statistics and Probabilities; Applications in finance, insurance, geosciences; Multivariate extreme value theory; Risk assessment)
Salibian-Barrera, Matias (S-regression estimationg, robust statistics, functional principal component analysis, bootstrap estimators, rgam, clustering algorithm)
Welch, William (, Design of experiments, experiments with computer models, data mining, drug discovery, quality improvement)
Wu, Lang (Biostatistics)
Zamar, Ruben (Data mining and text mining, Modeling data quality, Development of new robust procedures, Statistical computing, Bioinformatics)
Sample Thesis Submissions
Research interests of the faculty include biostatistics, environmetrics, mathematical modelling of biological systems, computational statistics, data mining, machine learning, theory of statistical inference, asymptotics, multivariate analysis, robustness, nonparametrics, design of experiments, smoothing, Bayesian methods, computational molecular biology, gene expression, and microarrays.