Please note:
To view the Spring 2021 Academic Calendar, go to www.sfu.ca/students/calendar/2021/spring.html.
Statistics Major
The department offers a bachelor of science (BSc) program in statistics within the Faculty of Science.
Frequently asked questions about the program and contact information for the Undergraduate Advisor for the department can be found here: https://www.sfu.ca/statactsci/undergraduate/currentstudents/advising.html. Students should seek program planning advice early in their academic careers.
Admission Requirements
Students may be admitted by direct entry on their university application, or by application to the Department of Statistics, after they have been admitted.
Visit https://www.sfu.ca/statactsci/undergraduate/prospectivestudents/admissionstransferring/admissionstatistics.html to view admission requirements.
Courses for Further Credit
Once a STAT course has been completed with a grade of C or higher, STAT courses that are prerequisites to this course may not be taken for further credit without permission of the department.
Computing Recommendation
Experience with a high level programming language is recommended by the start of the second year.
Prerequisite Grade Requirement
Students must have a grade of C or better in prerequisites for STAT courses.
GPA Required for Continuation
To continue in the program, students must maintain at least a 2.25 grade point average in MATH, STAT, MACM and CMPT courses.
Graduation Grade Point Averages
See required GPA for graduation from the Statistics major program.
Accreditation of Courses
The Statistical Society of Canada has accredited certain courses within the department for partial fulfillment of the educational requirements for the associate statistician (AStat) designation. The list of accredited courses is available at https://ssc.ca/sites/default/files/imce/pdf/accreditationcourselistsimonfraser.pdf. Please contact the department for details. Further information on the professional statistician (PStat) and associate statistician (AStat) designations is available at https://www.ssc.ca/en/accreditation.
Program Requirements
Students complete 120 units, including the lower division, upper division, and additional upper division requirements specified below.
Lower Division Requirements
Students complete the following courses:
One of*
A programming course which will provide the science student with a working knowledge of a scientific programming language and an introduction to computing concepts, structured programming, and modular design. The student will also gain knowledge in the use of programming environments including the use of numerical algorithm packages. Corequisite: MATH 152 or 155 (or 158). Students with credit for CMPT 120, 128, 130 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129 or 135 first may not then take this course for further credit. Quantitative.
An elementary introduction to computing science and computer programming, suitable for students with little or no programming background. Students will learn fundamental concepts and terminology of computing science, acquire elementary skills for programming in a highlevel language and be exposed to diverse fields within, and applications of computing science. Topics will include: pseudocode, data types and control structures, fundamental algorithms, computability and complexity, computer architecture, and history of computing science. Treatment is informal and programming is presented as a problemsolving tool. Prerequisite: BC Math 12 or equivalent is recommended. Students with credit for CMPT 102, 128, 130 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129, 130 or 135 first may not then take this course for further credit. Quantitative/BreadthScience.
Section  Instructor  Day/Time  Location 

D100 
Diana Cukierman 
Mo, We, Fr 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 
D200 
Diana Cukierman 
Mo, We, Fr 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 
and one of*
A rigorous introduction to computing science and computer programming, suitable for students who already have some background in computing science and programming. Intended for students who will major in computing science or a related program. Topics include: fundamental algorithms; elements of empirical and theoretical algorithmics; abstract data types and elementary data structures; basic objectoriented programming and software design; computation and computability; specification and program correctness; and history of computing science. Prerequisite: CMPT 120 with a minimum grade of C. Corequisite: CMPT 127. Students with credit for CMPT 126, 129, 135 or CMPT 200 or higher may not take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
John Edgar 
Mo, We, Fr 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 
A second course in computing science and programming intended for students studying mathematics, statistics or actuarial science and suitable for students who already have some background in computing science and programming. Topics include: a review of the basic elements of programming: use and implementation of elementary data structures and algorithms; fundamental algorithms and problem solving; basic objectoriented programming and software design; computation and computability and specification and program correctness. Prerequisite: CMPT 102 or CMPT 120, with a minimum grade of C. Students with credit for CMPT 125 or 135 may not take this course for further credit. Quantitative.
and one of
Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Topics as for Math 151 with a more extensive review of functions, their properties and their graphs. Recommended for students with no previous knowledge of Calculus. In addition to regularly scheduled lectures, students enrolled in this course are encouraged to come for assistance to the Calculus Workshop (Burnaby), or Math Open Lab (Surrey). Prerequisite: PreCalculus 12 (or equivalent) with a grade of at least B+, or MATH 100 with a grade of at least B, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 151, 154 or 157 may not take MATH 150 for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Mo, We, Fr 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 

OP01  TBD 
Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Logarithmic and exponential functions, trigonometric functions, inverse functions. Limits, continuity, and derivatives. Techniques of differentiation, including logarithmic and implicit differentiation. The Mean Value Theorem. Applications of differentiation including extrema, curve sketching, Newton's method. Introduction to modeling with differential equations. Polar coordinates, parametric curves. Prerequisite: PreCalculus 12 (or equivalent) with a grade of at least A, or MATH 100 with a grade of at least B, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 154 or 157 may not take MATH 151 for further credit. Quantitative.
Designed for students specializing in the biological and medical sciences. Topics include: limits, growth rate and the derivative; elementary functions, optimization and approximation methods, and their applications; mathematical models of biological processes. Prerequisite: PreCalculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 151 or 157 may not take MATH 154 for further credit. Quantitative.
Designed for students specializing in business or the social sciences. Topics include: limits, growth rate and the derivative; logarithmic, exponential and trigonometric functions and their application to business, economics, optimization and approximation methods; introduction to functions of several variables with emphasis on partial derivatives and extrema. Prerequisite: PreCalculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 151 or 154 may not take MATH 157 for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Justin Chan 
Mo, We, Fr 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 
OP01  TBD 
and one of
Riemann sum, Fundamental Theorem of Calculus, definite, indefinite and improper integrals, approximate integration, integration techniques, applications of integration. Firstorder separable differential equations and growth models. Sequences and series, series tests, power series, convergence and applications of power series. Prerequisite: MATH 150 or 151; or MATH 154 or 157 with a grade of at least B. Students with credit for MATH 155 or 158 may not take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Vijaykumar Singh 
Mo, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
OP01  TBD 
Designed for students specializing in the biological and medical sciences. Topics include: the integral, partial derivatives, differential equations, linear systems, and their applications; mathematical models of biological processes. Prerequisite: MATH 150, 151 or 154; or MATH 157 with a grade of at least B. Students with credit for MATH 152 or 158 may not take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Mahdieh Malekian 
Mo, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
OPO1  TBD 
Designed for students specializing in business or the social sciences. Topics include: theory of integration, integration techniques, applications of integration; functions of several variables with emphasis on double and triple integrals and their applications; introduction to differential equations with emphasis on some special firstorder equations and their applications; sequences and series. Prerequisite: MATH 150 or 151 or 154 or 157, with a minimum grade of C. Students with credit for MATH 152 or 155 may not take MATH 158 for further credit. Quantitative.
and
A seminar primarily for students undertaking a major or an honours program in Statistics. Visiting speakers share experience relevant to Statistics students and provide useful education and career advice. Prerequisite: Enrollment in the Statistics or Actuarial Science major or honours program, or STAT 270 with a minimum grade of C.
and one of
Linear equations, matrices, determinants. Introduction to vector spaces and linear transformations and bases. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. An emphasis on applications involving matrix and vector calculations. Prerequisite: MATH 150 or 151; or MACM 101; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 240 make not take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D200 
Justin Chan 
Mo, We, Fr 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 
OP01  TBD 
Linear equations, matrices, determinants. Real and abstract vector spaces, subspaces and linear transformations; basis and change of basis. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. Applications. Subject is presented with an abstract emphasis and includes proofs of the basic theorems. Prerequisite: MATH 150 or 151; or MACM 101; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 232 cannot take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Mo, We, Fr 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 

D101 
Th 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 

D102 
Th 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D103 
Th 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 
and all of
Rectangular, cylindrical and spherical coordinates. Vectors, lines, planes, cylinders, quadric surfaces. Vector functions, curves, motion in space. Differential and integral calculus of several variables. Vector fields, line integrals, fundamental theorem for line integrals, Green's theorem. Prerequisite: MATH 152; or MATH 155 or MATH 158 with a grade of at least B. Recommended: It is recommended that MATH 240 or 232 be taken before or concurrently with MATH 251. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Steven Ruuth 
Mo, We, Fr 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 
D200 
Jamie Mulholland 
Mo, We, Fr 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 
OP01  TBD  
OP02  TBD 
Introduction to modern tools and methods for data acquisition, management, and visualization capable of scaling to Big Data. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, or BUEC 232, and one of CMPT 102, CMPT 120, CMPT 125, CMPT 128, CMPT 129, CMPT 130, all with a minimum grade of C or permission of the instructor. STAT 260 is also recommended. Quantitative.
An introduction to the R programming language for data science. Exploring data: visualization, transformation and summaries. Data wrangling: reading, tidying, and data types. No prior computer programming experience required. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or POL 201, with a grade of at least C or permission of the instructor. Corequisite: STAT 261. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.
A handson application of the R programming language for data science. Using the R concepts covered in STAT 260, students will explore (visualize, transform, and summarize) and wrangle (read and tidy) data. No prior computer programming experience required. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or POL 201, with a grade of at least C or permission of the instructor. Corequisite: STAT 260. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.
Basic laws of probability, sample distributions. Introduction to statistical inference and applications. Prerequisite: or Corequisite: MATH 152 or 155 or 158, with a minimum grade of C. Students wishing an intuitive appreciation of a broad range of statistical strategies may wish to take STAT 100 first. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Scott Pai 
We 11:30 AM – 12:20 PM Fr 10:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby REMOTE LEARNING, Burnaby 
OP01  TBD 
This course is a continuation of STAT 270. Review of probability models. Procedures for statistical inference using survey results and experimental data. Statistical model building. Elementary design of experiments. Regression methods. Introduction to categorical data analysis. Prerequisite: STAT 270 and one of MATH 152, MATH 155, or MATH 158, all with a minimum grade of C. Quantitative.
* Students are strongly encouraged to complete this requirement in their first year.
** Recommended. Students with prior computing experience may be able to challenge CMPT 120.
*** CMPT 127 is a corequisite.
**** Recommended.
Upper Division Requirements
Students complete all of
Guided experiences in written and oral communication of statistical ideas and results with both scientific and lay audiences. Prerequisite: Admission to the major or honours programs in statistics; STAT 350 with a minimum grade of C or permission of the instructor; prior completion of a lower division W course. Writing.
Section  Instructor  Day/Time  Location 

D100 
Sessional 
Tu 10:30 AM – 12:20 PM Fr 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby REMOTE LEARNING, Burnaby 
Review of probability and distributions. Multivariate distributions. Distributions of functions of random variables. Limiting distributions. Inference. Sufficient statistics for the exponential family. Maximum likelihood. Bayes estimation, Fisher information, limiting distributions of MLEs. Likelihood ratio tests. Prerequisite: STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C. Quantitative.
Introduces the SAS statistical package. Data management; reading, editing and storing statistical data; data exploration and representation; summarizing data with tables, graphs and other statistical tools; and data simulation. Prerequisite: STAT 285 or STAT 302 or STAT 305 or BUEC 333, with a minimum grade of C. Students with credit for STAT 340 may not take STAT 342 for further credit.
Theory and application of linear regression. Normal distribution theory. Hypothesis tests and confidence intervals. Model selection. Model diagnostics. Introduction to weighted least squares and generalized linear models. Prerequisite: STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C. Quantitative.
and 12 units in upper division STAT courses from Lists A and B (including a minimum of two courses from List A)
and nine units in additional upper division ACMA, MACM, MATH or STAT courses from Lists A and B. STAT courses (STAT 360 and STAT 361 in particular) and MACM 316 are recommended.
List A
Review of discrete and continuous probability models and relationships between them. Exploration of conditioning and conditional expectation. Markov chains. Random walks. Continuous time processes. Poisson process. Markov processes. Gaussian processes. Prerequisite: STAT 330, or all of: STAT 285, MATH 208W, and MATH 251, all with a minimum grade of C. Quantitative.
Topics in areas of probability and statistics not covered in the regular undergraduate curriculum of the department. Prerequisite: Dependent on the topic covered.
An introduction to the major sample survey designs and their mathematical justification. Associated statistical analyses. Prerequisite: STAT 350 with a minimum grade of C. Quantitative.
A datafirst discovery of advanced statistical methods. Focus will be on a series of forecasting and prediction competitions, each based on a large realworld dataset. Additionally, practical tools for statistical modeling in realworld environments will be explored. Prerequisite: 90 units including STAT 350 with a minimum grade of C and one of STAT 341, STAT 260, or CMPT 225, with a minimum grade of C, or instructor approval. STAT 240 is also recommended.
Distribution theory, methods for constructing tests, estimators, and confidence intervals with special attention to likelihood methods. Properties of the procedures including large sample theory. Prerequisite: STAT 330 with a minimum grade of C. Quantitative.
The Bayesian approach to statistics is an alternative and increasingly popular way of quantifying uncertainty in the presence of data. This course considers comparative statistical inference, prior distributions, Bayesian computation, and applications. Prerequisite: STAT 330 and 350, with a minimum grade of C. Quantitative.
Topics in areas of probability and statistics not covered in the regular undergraduate curriculum of the department. Prerequisite: Dependent on the topic covered.
Independent reading or research on consultation with the supervising instructor. This course can be repeated for credit. Prerequisite: Written permission of the department undergraduate studies committee.
List B
Advanced R programming methods for data science. Tools for reproducible research. Version control. Data structures, subsetting, functions, environments, and debugging. Functional programming. Code performance: profiling, memory, integrating R and C++. Prerequisite: One of STAT 260 or STAT 341 and one of STAT 302, STAT 305, STAT 350, or ECON 333, all with a minimum grade of C. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 361.
A handson application of advanced R programming methods for data science. Using the R concepts covered in STAT 360 and tools for reproducible research, students will work with different data structures, write functions, and debug and optimize the performance of their code. Prerequisite: One of STAT 260 or STAT 341 and one of STAT 302, STAT 305, STAT 350, or ECON 333, all with a minimum grade of C. CMPT 125 or CMPT 129 is also recommended. Corequisite: STAT 360.
An introduction to the essential modern supervised and unsupervised statistical learning methods. Topics include review of linear regression, classification, statistical error measurement, flexible regression and classification methods, clustering and dimension reduction. Prerequisite: STAT 302 or STAT 305 or STAT 350 or BUEC 333 or equivalent, with a minimum grade of C. Quantitative.
Introduction to standard methodology for analyzing categorical data including chisquared tests for two and multiway contingency tables, logistic regression, and loglinear (Poisson) regression. Prerequisite: STAT 302 or STAT 305 or STAT 350 or BUEC 333 or equivalent, with a minimum grade of C. Students with credit for the former STAT 402 or 602 may not take this course for further credit. Quantitative.
Introduction to linear time series analysis including moving average, autoregressive and ARIMA models, estimation, data analysis, forecasting errors and confidence intervals, conditional and unconditional models, and seasonal models. Prerequisite: STAT 285 or STAT 302 or STAT 305 or BUEC 333 or equivalent, with a minimum grade of C. This course may not be taken for further credit by students who have credit for ECON 484. Quantitative.
For students who wish to seek accreditation with the Statistical Society of Canada, STAT 450 and at least one of STAT 410 or STAT 430 are recommended.
Additional Upper Division Requirements
Students must complete 12 additional upper division units to satisfy university requirements. Any upper division nonSTAT courses or STAT courses from Lists A and B above may be used to complete these units.
University Degree Requirements
Students must also satisfy University degree requirements for degree completion.
Writing, Quantitative, and Breadth Requirements
Students admitted to Simon Fraser University beginning in the fall 2006 term must meet writing, quantitative and breadth requirements as part of any degree program they may undertake. See Writing, Quantitative, and Breadth Requirements for universitywide information.
WQB Graduation Requirements
A grade of C or better is required to earn W, Q or B credit
Requirement 
Units 
Notes  
W  Writing 
6 
Must include at least one upper division course, taken at Simon Fraser University within the student’s major subject  
Q  Quantitative 
6 
Q courses may be lower or upper division  
B  Breadth 
18 
Designated Breadth  Must be outside the student’s major subject, and may be lower or upper division 6 units Social Sciences: BSoc 6 units Humanities: BHum 6 units Sciences: BSci 
6 
Additional Breadth  6 units outside the student’s major subject (may or may not be Bdesignated courses, and will likely help fulfil individual degree program requirements) Students choosing to complete a joint major, joint honours, double major, two extended minors, an extended minor and a minor, or two minors may satisfy the breadth requirements (designated or not designated) with courses completed in either one or both program areas. 
Residency Requirements and Transfer Credit
 At least half of the program's total units must be earned through Simon Fraser University study.
 At least two thirds of the program's total upper division units must be earned through Simon Fraser University study.
Elective Courses
In addition to the courses listed above, students should consult an academic advisor to plan the remaining required elective courses.