Please note:

To view the Spring 2024 Academic Calendar, go to www.sfu.ca/students/calendar/2024/spring.html.

Department of Statistics and Actuarial Science | Faculty of Science Simon Fraser University Calendar | Summer 2024

Statistics Honours

Bachelor of Science

The department offers a bachelor of science (BSc) honours 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/stat-actsci/undergraduate/current-students/advising.html. Students should seek advice early in their academic careers about program planning from the department's advisors.

Admission Requirements

Students may be admitted by application to the Department of Statistics, after they have been admitted.

Visit https://www.sfu.ca/stat-actsci/undergraduate/prospective-students/admissions/admission-statistics.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

Some experience with a high level programming language is recommended by the beginning of the second year.

Prerequisite Grade Requirement

Students must have a grade of C- or better in prerequisites for STAT course.

GPA Required for Continuation

To continue in the program, students must maintain at least a 3.00 grade point average (GPA) in MATH, STAT, MACM and CMPT courses.

Graduation Grade Point Averages

See required GPA for graduation from the Statistics honours 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/2021_sfu_-_courses-sfu-summary-2021_1.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:

Both of

CMPT 120 - Introduction to Computing Science and Programming I (3) *

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 high-level language, e.g. Python. The students will be exposed to diverse fields within, and applications of computing science. Topics will include: pseudocode; data types and control structures; fundamental algorithms; recursion; reading and writing files; measuring performance of algorithms; debugging tools; basic terminal navigation using shell commands. Treatment is informal and programming is presented as a problem-solving 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/Breadth-Science.

Section Instructor Day/Time Location
D100 Gregory Baker
May 6 – Aug 2, 2024: Mon, Wed, Fri, 10:30–11:20 a.m.
Burnaby
CMPT 125 - Introduction to Computing Science and Programming II (3)

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: memory management; fundamental algorithms; formally analyzing the running time of algorithms; abstract data types and elementary data structures; object-oriented programming and software design; specification and program correctness; reading and writing files; debugging tools; shell commands. Prerequisite: CMPT 120 or CMPT 130, with a minimum grade of C-. 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 Anne Lavergne
May 6 – Aug 2, 2024: Mon, Wed, Fri, 11:30 a.m.–12:20 p.m.
Burnaby
D101 Anne Lavergne
May 6 – Aug 2, 2024: Thu, 10:30–11:20 a.m.
Burnaby
D102 Anne Lavergne
May 6 – Aug 2, 2024: Thu, 10:30–11:20 a.m.
Burnaby
D103 Anne Lavergne
May 6 – Aug 2, 2024: Thu, 11:30 a.m.–12:20 p.m.
Burnaby
D104 Anne Lavergne
May 6 – Aug 2, 2024: Thu, 11:30 a.m.–12:20 p.m.
Burnaby
D105 Anne Lavergne
May 6 – Aug 2, 2024: Thu, 12:30–1:20 p.m.
Burnaby
D106 Anne Lavergne
May 6 – Aug 2, 2024: Thu, 12:30–1:20 p.m.
Burnaby
D107 Anne Lavergne
May 6 – Aug 2, 2024: Thu, 1:30–2:20 p.m.
Burnaby
D108 Anne Lavergne
May 6 – Aug 2, 2024: Thu, 1:30–2:20 p.m.
Burnaby

and one of

MATH 150 - Calculus I with Review (4)

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: Pre-Calculus 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 Mahsa Faizrahnemoon
May 6 – Aug 2, 2024: Mon, Wed, Fri, 1:30–2:20 p.m.
Burnaby
D101 May 6 – Aug 2, 2024: Tue, 8:30–9:20 a.m.
Burnaby
D102 May 6 – Aug 2, 2024: Tue, 9:30–10:20 a.m.
Burnaby
D103 May 6 – Aug 2, 2024: Tue, 10:30–11:20 a.m.
Burnaby
OP01 TBD
MATH 151 - Calculus I (3)

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: Pre-Calculus 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.

MATH 154 - Mathematics for the Life Sciences I (3)

Designed for students specializing in the life sciences. Topics include: limits, growth rate and the derivative; elementary functions, optimization and approximation methods, and their applications, integration, and differential equations; mathematical models of biological processes and their implementation and analysis using software. Prerequisite: Pre-Calculus 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.

MATH 157 - Calculus I for the Social Sciences (3)

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: Pre-Calculus 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 Paul Tupper
May 6 – Aug 2, 2024: Mon, Wed, Fri, 11:30 a.m.–12:20 p.m.
Burnaby
OP01 TBD

and one of

MATH 152 - Calculus II (3)

Riemann sum, Fundamental Theorem of Calculus, definite, indefinite and improper integrals, approximate integration, integration techniques, applications of integration. First-order separable differential equations and growth models. Sequences and series, series tests, power series, convergence and applications of power series. Prerequisite: MATH 150 or 151, with a minimum grade of C-; 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 Stephen Choi
May 6 – Aug 2, 2024: Mon, Wed, Fri, 8:30–9:20 a.m.
Burnaby
OP01 TBD
MATH 155 - Mathematics for the Life Sciences II (3)

Designed for students specializing in the life sciences. Topics include: vectors and matrices, partial derivatives, multi-dimensional integrals, systems of differential equations, compartment models, graphs and networks, and their applications to the life sciences; mathematical models of multi-component biological processes and their implementation and analysis using software. Prerequisite: MATH 150, 151 or 154, with a minimum grade of C-; 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 Veselin Jungic
May 6 – Aug 2, 2024: Mon, Wed, Fri, 8:30–9:20 a.m.
Burnaby
OPO1 TBD
MATH 158 - Calculus II for the Social Sciences (3)

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 first-order 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

STAT 180 - Career Development Seminar for Statistics and Actuarial Science (1)

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

MATH 232 - Applied Linear Algebra (3)

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, with a minimum grade of C-; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 240 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Justin Chan
May 6 – Aug 2, 2024: Mon, Wed, Fri, 1:30–2:20 p.m.
Surrey
OP01 TBD
MATH 240 - Algebra I: Linear Algebra (3) **

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, with a minimum grade of C-; 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 Imin Chen
May 6 – Aug 2, 2024: Mon, Wed, Fri, 11:30 a.m.–12:20 p.m.
Burnaby
D101 May 6 – Aug 2, 2024: Thu, 9:30–10:20 a.m.
Burnaby
D102 May 6 – Aug 2, 2024: Thu, 2:30–3:20 p.m.
Burnaby
D103 May 6 – Aug 2, 2024: Thu, 3:30–4:20 p.m.
Burnaby

and all of

MATH 242 - Introduction to Analysis I (3)

Mathematical induction. Limits of real sequences and real functions. Continuity and its consequences. The mean value theorem. The fundamental theorem of calculus. Series. Prerequisite: MATH 152 with a minimum grade of C-; or MATH 155 or 158 with a grade of B. Quantitative.

MATH 251 - Calculus III (3)

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 with a minimum grade of C-; 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 Paul Tupper
May 6 – Aug 2, 2024: Mon, Wed, Fri, 1:30–2:20 p.m.
Burnaby
D200 Randall Pyke
May 6 – Aug 2, 2024: Mon, Wed, Fri, 9:30–10:20 a.m.
Surrey
OP01 TBD
OP02 TBD
STAT 240 - Introduction to Data Science (3)

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, BUS 232, or ECON 233, 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.

STAT 260 - Introductory R for Data Science (2)

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, ECON 233, 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.

STAT 261 - Laboratory for Introductory R for Data Science (1)

A hands-on 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, ECON 233, 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.

STAT 270 - Introduction to Probability and Statistics (3)

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 Sessional
May 6 – Aug 2, 2024: Wed, 11:30 a.m.–12:20 p.m.
May 6 – Aug 2, 2024: Fri, 10:30 a.m.–12:20 p.m.
Burnaby
Burnaby
OL01 Gamage Perera
Online
OP01 TBD
STAT 285 - Intermediate Probability and Statistics (3)

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. Students with prior computing experience may be able to challenge CMPT 120.

** Recommended.

Upper Division Requirements

Students complete all of

MATH 320 - Introduction to Analysis II (3)

Sequences and series of functions, topology of sets in Euclidean space, introduction to metric spaces, functions of several variables. Prerequisite: MATH 242 and 251, with a minimum grade of C-. Quantitative.

MATH 322 - Complex Variables (3)

Functions of a complex variable, differentiability, contour integrals, Cauchy's theorem, Taylor and Laurent expansions, method of residues. Prerequisite: MATH 251 with a minimum grade of C-. Students with credit for MATH 424 may not take this course for further credit. Quantitative.

STAT 300W - Statistics Communication (3)

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
May 6 – Aug 2, 2024: Tue, 10:30 a.m.–12:20 p.m.
May 6 – Aug 2, 2024: Fri, 10:30–11:20 a.m.
Burnaby
Burnaby
STAT 330 - Introduction to Mathematical Statistics (3)

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.

STAT 342 - Introduction to Statistical Computing and Exploratory Data Analysis - SAS (2)

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 ECON 333, with a minimum grade of C-. Students with credit for STAT 340 may not take STAT 342 for further credit.

STAT 350 - Linear Models in Applied Statistics (3)

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.

STAT 380 - Introduction to Stochastic Processes (3)

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.

STAT 410 - Statistical Analysis of Sample Surveys (3)

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.

STAT 430 - Statistical Design and Analysis of Experiments (3)

An extension of the designs discussed in STAT 350 to include more than one blocking variable, incomplete block designs, fractional factorial designs, and response surface methods. Prerequisite: STAT 350 with a minimum grade of C-. Quantitative.

STAT 450 - Statistical Theory (3)

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.

STAT 460 - Bayesian Statistics (3)

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.

STAT 475 - Applied Discrete Data Analysis (3)

Introduction to standard methodology for analyzing categorical data including chi-squared tests for two- and multi-way contingency tables, logistic regression, and loglinear (Poisson) regression. Prerequisite: STAT 302 or STAT 305 or STAT 350 or ECON 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.

and nine units in additional upper division ACMA, MACM, MATH or STAT courses from List A below. STAT courses (STAT 360 and STAT 361 in particular) and MACM 316 are recommended.

List A

STAT 360 - Advanced R for Data Science (2)

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.

STAT 361 - Laboratory for Advanced R for Data Science (1)

A hands-on 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.

STAT 390 - Selected Topics in Probability and Statistics (3)

Topics in areas of probability and statistics not covered in the regular undergraduate curriculum of the department. Prerequisite: Dependent on the topic covered.

STAT 440 - Learning from Big Data (3)

A data-first discovery of advanced statistical methods. Focus will be on a series of forecasting and prediction competitions, each based on a large real-world dataset. Additionally, practical tools for statistical modeling in real-world 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.

STAT 445 - Applied Multivariate Analysis (3)

Introduction to principal components, cluster analysis, and other commonly used multivariate techniques. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

STAT 452 - Statistical Learning and Prediction (3)

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 ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

STAT 485 - Applied Time Series Analysis (3)

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 ECON 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.

STAT 490 - Selected Topics in Probability and Statistics (3)

Topics in areas of probability and statistics not covered in the regular undergraduate curriculum of the department. Prerequisite: Dependent on the topic covered.

STAT 495 - Directed Studies in Probability and Statistics (3)

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.

Additional Upper Division Requirements

Students must complete 16 additional upper division units to satisfy university requirements. Any upper division non-STAT courses or STAT courses from List A above may be used to complete these units.

University Honours 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 university-wide 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; two courses (minimum three units each)

Q - Quantitative

6

Q courses may be lower or upper division; two courses (minimum three units each)
B - Breadth

18

Designated Breadth

Must be outside the student's major subject, and may be lower or upper division:

Two courses (minimum three units each) Social Sciences: B-Soc
Two courses (minimum three units each) Humanities: B-Hum
Two courses (minimum three units each) Sciences: B-Sci

6

Additional Breadth

Two courses (minimum three units each) outside the student's major subject (may or may not be B-designated 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.