Please note:

To view the Spring 2021 Academic Calendar, go to www.sfu.ca/students/calendar/2021/spring.html.

Department of Statistics and Actuarial Science | Faculty of Science Simon Fraser University Calendar | Summer 2021

Data Science Honours

Bachelor of Science

The Department of Statistics and Actuarial Science and its partners, the Department of Mathematics, the Beedie School of Business, and the School of Computing Science, offer an honours program in Data Science (DATA) leading to a bachelor of science (BSc) with honours degree. This is a highly structured program providing a multidisciplinary approach to quantitative methods for business and industry in an environment of rapid changes in technology. The honours program offers specialization in one of three concentrations: Mathematics, Statistics, or Open Concentration.

The program is managed by a steering committee consisting of representatives from the above-mentioned departments, and faculty serve as liaisons between participating departments and the program director.

Students formally apply to be admitted into the program. Applications can be considered both for students entering Simon Fraser University, and for students already enrolled. Admission into the program is decided on a competitive basis. Students must maintain a 3.0 cumulative grade point average (CGPA) in DATA program course work to remain in the program and to graduate. It is strongly recommended that students contact the Statistics advisor or program director early about admission and scheduling.

Students who wish to combine the DATA honours program with another major or minor program should consult with the Statistics advisor.

More information can be found on our website: https://www.sfu.ca/stat-actsci/undergraduate/current-students/program-info/data-science.html.

Program Requirements

Under University regulations, an honours degree requires the completion of a minimum of 120 units, including a minimum of 60 upper division units. Honours program students require a graduation cumulative grade point average of not less than 3.00.

Mathematics Concentration Requirements

Lower Division Requirements

Students complete a minimum of 68 units.

Business Administration

Students complete all of

BUS 200 - Business Fundamentals (3)

Explore the fundamentals of modern business and organizational management. Working with case studies, students will build upon the basics of revenue, profits, contribution and costs, as well as integrate advanced aspects of business models, innovation, competitive advantage, core competence, and strategic analysis. Students with credit for BUS 130 or 201 may not receive further credit for this course. Breadth-Social Sciences.

Section Instructor Day/Time Location
D200 Sasha Ramnarine
Mo 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
E100 Tracy Yiu
We 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
BUS 217W - Critical Thinking in Business (3)

Examine and review today's global economy through critical analysis of differing perspectives. Develop and improve critical thinking and communication skills appropriate to the business environment. Prerequisite: BUS 201 with a minimum grade of C- and 15 units; OR 45 units and corequisite: BUS 202; OR Business Administration joint major, joint honours, or double degree students with 45 units; OR Data Science majors with 15 units. Writing.

Section Instructor Day/Time Location
D100 Susan Christie-Bell
Tu 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D200 Luana Carcano
We 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D300 Luana Carcano
Th 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D400 Jane McCarthy
Fr 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D500 Brent Amburgey
Tu 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
E100 Darelle Odo
Th 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E200 Brent Amburgey
We 4:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
BUS 251 - Financial Accounting I (3)

An introduction to financial accounting, including accounting terminology, understanding financial statements, analysis of a business entity using financial statements. Includes also time value of money and a critical review of the conventional accounting system. Prerequisite: 12 units. Quantitative.

Section Instructor Day/Time Location
D100 Susan Bubra
Mo 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D101 Mo 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D102 Mo 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D103 Mo 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D104 Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D105 Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D200 Susan Bubra
Mo 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D201 Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D202 Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D203 Mo 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D204 Mo 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D205 Mo 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
BUS 272 - Behaviour in Organizations (3)

Theories, concepts and issues in the field of organizational behaviour with an emphasis on individual and team processes. Core topics include employee motivation and performance, stress management, communication, work perceptions and attitudes, decision-making, team dynamics, employee involvement and conflict management. Prerequisite: 12 units.

Section Instructor Day/Time Location
D100 Bahareh Assadi
Th 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D101 Th 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D102 Th 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D103 Th 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D104 Th 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D105 Th 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D106 Th 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D107 Th 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
E100 Sam Thiara
Tu 4:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby
E101 Tu 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
E102 Tu 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
E103 Tu 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
E104 Tu 7:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E105 Tu 7:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E106 Tu 8:30 PM – 9:20 PM
REMOTE LEARNING, Burnaby

Computing Science

Students complete all 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 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 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 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
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: fundamental algorithms; elements of empirical and theoretical algorithmics; abstract data types and elementary data structures; basic object-oriented 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
Toby Donaldson
Mo, We, Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
CMPT 127 - Computing Laboratory (3)

Builds on CMPT 120 to give a hands-on introduction to programming in C and C++, the basics of program design, essential algorithms and data structures. Guided labs teach the standard tools and students exploit these ideas to create software that works. To be taken in parallel with CMPT 125. Prerequisite: CMPT 120 or CMPT 128 or CMPT 130, with a minimum grade of C-. Corequisite: CMPT 125.

Section Instructor Day/Time Location
D100 Alice Yue
Tu 8:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D200 Alice Yue
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D300 Alice Yue
Tu 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
CMPT 225 - Data Structures and Programming (3)

Introduction to a variety of practical and important data structures and methods for implementation and for experimental and analytical evaluation. Topics include: stacks, queues and lists; search trees; hash tables and algorithms; efficient sorting; object-oriented programming; time and space efficiency analysis; and experimental evaluation. Prerequisite: (MACM 101 and ((CMPT 125 and 127), CMPT 129 or CMPT 135)) or (ENSC 251 and ENSC 252), all with a minimum grade of C-. Quantitative.

Section Instructor Day/Time Location
D100 John Edgar
Mo, We, Fr 9:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D101 John Edgar
We 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D102 John Edgar
We 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D103 John Edgar
We 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D104 John Edgar
We 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D105 John Edgar
We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D106 John Edgar
We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D107 John Edgar
We 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D108 John Edgar
We 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
CMPT 276 - Introduction to Software Engineering (3)

An overview of various techniques used for software development and software project management. Major tasks and phases in modern software development, including requirements, analysis, documentation, design, implementation, testing,and maintenance. Project management issues are also introduced. Students complete a team project using an iterative development process. Prerequisite: One W course, CMPT 225, (MACM 101 or (ENSC 251 and ENSC 252)) and (MATH 151 or MATH 150), all with a minimum grade of C-. MATH 154 or MATH 157 with at least a B+ may be substituted for MATH 151 or MATH 150. Students with credit for CMPT 275 may not take this course for further credit.

Section Instructor Day/Time Location
D100 Bobby Chan
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D200 Herbert Tsang
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
CMPT 295 - Introduction to Computer Systems (3)

The curriculum introduces students to topics in computer architecture that are considered fundamental to an understanding of the digital systems underpinnings of computer systems. Prerequisite: Either (MACM 101 and ((CMPT 125 and CMPT 127) or CMPT 135)) or (MATH 151 and CMPT 102 for students in an Applied Physics program), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Arrvindh Shriraman
Mo 12:30 PM – 1:20 PM
Th 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
D101 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D102 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D103 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D104 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D105 Arrvindh Shriraman
Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D106 Arrvindh Shriraman
Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby

Mathematics and Computing Science

Students complete all of

MACM 101 - Discrete Mathematics I (3)

Introduction to counting, induction, automata theory, formal reasoning, modular arithmetic. Prerequisite: BC Math 12 (or equivalent), or any of MATH 100, 150, 151, 154, 157. Quantitative/Breadth-Science.

MACM 201 - Discrete Mathematics II (3)

A continuation of MACM 101. Topics covered include graph theory, trees, inclusion-exclusion, generating functions, recurrence relations, and optimization and matching. Prerequisite: MACM 101 or (ENSC 251 and one of MATH 232 or MATH 240). Quantitative.

MACM 203 - Computing with Linear Algebra (2)

Using a mathematical software package for doing calculations in linear algebra. Development of computer models that analyze and illustrate applications of linear algebra. All calculations and experiments will be done in the Matlab software package. Topics include: large-scale matrix calculations, experiments with cellular automata, indexing, searching and ranking pages on the internet, population models, data fitting and optimization, image analysis, and cryptography. Prerequisite: One of CMPT 102, 120, 126, 128 or 130 and one of MATH 150, 151, 154 or 157 and one of MATH 232 or 240. MATH 232 or 240 can be taken as corequisite. Students in excess of 80 units may not take MACM 203 for further credit. Quantitative.

MACM 204 - Computing with Calculus (2)

Using a mathematical software package for doing computations from calculus. Development of computer models that analyze and illustrate applications of calculus. All calculations and experiments will be done in the Maple software package. Topics include: graphing functions and data, preparing visual aids for illustrating mathematical concepts, integration, Taylor series, numerical approximation methods, 3D visualization of curves and surfaces, multi-dimensional optimization, differential equations and disease spread models. Prerequisite: One of CMPT 102, 120, 126, 128 or 130 and MATH 251. MATH 251 can be taken as a corequisite. Students in excess of 80 units may not take MACM 204 for further credit. Quantitative.

Data Science

Students complete

DATA 180 - Undergraduate Seminar in Data Science (1)

A seminar primarily for students undertaking a major or an honours program in Data Science. Prerequisite: Major or honours in Data Science or permission of the program director. Students with credit for DATA (or MSSC) 480 cannot receive credit for DATA (or MSSC) 180.

Mathematics

Students complete 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 Seyyed Aliasghar Hosseini
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, 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 - Calculus I for the Biological Sciences (3)

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: 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 Justin Chan
Mo, We, Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
OP01 TBD

and all 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 Vijaykumar Singh
Mo, We, Fr 8:30 AM – 9:20 AM
REMOTE LEARNING, Burnaby
OP01 TBD
MATH 208W - Introduction to Operations Research (3)

Introduction to methods of operations research: linear and nonlinear programming, simulation, and heuristic methods. Applications to transportation, assignment, scheduling, and game theory. Exposure to mathematical models of industry and technology. Emphasis on computation for analysis and simulation. Prerequisite: MATH 150 or 151 or 154 or 157, with a minimum grade of C-. Students with credit for MATH 208 may not take this course for further credit. Writing/Quantitative.

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 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

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
D200 Justin Chan
Mo, We, Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
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 Shuxing Li
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

Statistics

Students complete all of

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, 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.

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, 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, 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 Scott Pai
We 11:30 AM – 12:20 PM
Fr 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
OP01 TBD

* Recommended

Upper Division Requirements

Students complete a minimum of 50 units.

Business Administration

Students complete all of

BUS 343 - Introduction to Marketing (3)

The environment of marketing; relation of social sciences to marketing; evaluation of marketing theory and research; assessment of demand, consumer behaviour analysis; market institutions; method and mechanics of distribution in domestic, foreign and overseas markets; sales organization; advertising; new product development, publicity and promotion; marketing programs. Prerequisite: 45 units. Students with credit for COMM 343 may not take this course for further credit.

Section Instructor Day/Time Location
D100 Pei-Shiuan Lin
Fr 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D101 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D102 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D103 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D104 Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D105 Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D106 Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D107 Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D108 Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D109 Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D200 Pei-Shiuan Lin
Fr 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D201 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D202 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D203 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D204 Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D205 Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D206 Fr 11:30 AM – 12:30 PM
REMOTE LEARNING, Burnaby
D207 Fr 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D208 Fr 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D209 Fr 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
BUS 360W - Business Communication (4)

This course is designed to assist students to improve their written and oral communication skills in business settings. The theory and practice of business communication will be presented. Topics include analysis of communication problems, message character, message monitoring, message media. Exercises in individual and group messages and presentations will be conducted. Prerequisite: This course is open to students admitted prior to Fall 2014 to the Business Administration major, honours, or second degree program and who have 45 units, OR to students admitted Fall 2014 - Summer 2017 to the Business Administration major, honours, or second degree program and who have 45 units and BUS 130 or 201 or 202 or 301, with a minimum grade of C-, OR to student admitted Fall 2017 - onwards to the Business Administration major, honours, or second degree program and who have 45 units and BUS 130 or 201 or 202 or 301, with a minimum grade of C- and BUS 217W with a minimum grade of C-, OR to Business Administration joint major or joint honours students with BUS 217W with a minimum grade of C- and 45 units, OR to Business and Economics Joint Major students with ECON 220W with a minimum grade of C- and 45 units, OR to Mechatronic Systems Engineering and Business Administration double degree students with 45 units, OR to Management Systems Science or Actuarial Science majors with 45 units OR to Data Science major with BUS 217W with a minimum grade of C- and 45 units. Students who have taken BUS 360 may not take this course for further credit. Writing.

Section Instructor Day/Time Location
D100 Leanne Barlow
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D200 Leanne Barlow
Th 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D300 Christian Venhuizen
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D400 Christian Venhuizen
We 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D500 Susan Christie-Bell
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
E100 Eric Tung
We 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E200 Eric Tung
Th 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby

Computing Science

Students complete all of

CMPT 300 - Operating Systems I (3)

This course aims to give the student an understanding of what a modern operating system is, and the services it provides. It also discusses some basic issues in operating systems and provides solutions. Topics include multiprogramming, process management, memory management, and file systems. Prerequisite: CMPT 225 and (CMPT 295 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Harinder Khangura
Tu 12:30 PM – 2:20 PM
Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 307 - Data Structures and Algorithms (3)

Analysis and design of data structures for lists, sets, trees, dictionaries, and priority queues. A selection of topics chosen from sorting, memory management, graphs and graph algorithms. Prerequisite: CMPT 225, MACM 201, MATH 151 (or MATH 150), and MATH 232 or 240, all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 David Mitchell
Mo, We, Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
CMPT 353 - Computational Data Science (3)

Basic concepts and programming tools for handling and processing data. Includes data acquisition, cleaning data sources, application of machine learning techniques and data analysis techniques, large-scale computation on a computing cluster. Prerequisite: CMPT 225 and (STAT 101, STAT 270, ENSC 280, or MSE 210), with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Gregory Baker
Tu 10:30 AM – 12:20 PM
Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 354 - Database Systems I (3)

Logical representations of data records. Data models. Studies of some popular file and database systems. Document retrieval. Other related issues such as database administration, data dictionary and security. Prerequisite: CMPT 225 and (MACM 101 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Jack Thomas
Mo 8:30 AM – 9:20 AM
Th 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 454 - Database Systems II (3)

An advanced course on database systems which covers crash recovery, concurrency control, transaction processing, distributed database systems as the core material and a set of selected topics based on the new developments and research interests, such as object-oriented data models and systems, extended relational systems, deductive database systems, and security and integrity. Prerequisite: CMPT 300 and 354, with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Ouldooz Baghban Karimi
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby

Mathematics and Computing Science

MACM 316 - Numerical Analysis I (3)

A presentation of the problems commonly arising in numerical analysis and scientific computing and the basic methods for their solutions. Prerequisite: MATH 152 or 155 or 158, and MATH 232 or 240, and computing experience. Quantitative.

Section Instructor Day/Time Location
D100 Pengyu Liu
Mo, We, Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D101 Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D102 Mo 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D103 Tu 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D104 Tu 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D105 Tu 9:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D106 Tu 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D107 Mo 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D108 Mo 5:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby

Mathematics

Students complete one of

MATH 308 - Linear Optimization (3)

Linear programming modelling. The simplex method and its variants. Duality theory. Post-optimality analysis. Applications and software. Additional topics may include: game theory, network simplex algorithm, and convex sets. Prerequisite: MATH 150, 151, 154, or 157 and MATH 240 or 232, all with a minimum grade of C-. Quantitative.

MATH 309 - Continuous Optimization (3)

Theoretical and computational methods for investigating the minimum of a function of several real variables with and without inequality constraints. Applications to operations research, model fitting, and economic theory. Prerequisite: MATH 232 or 240, and 251, all with a minimum grade of C-. Quantitative.

and one of

MACM 409 - Numerical Linear Algebra: Algorithms, Implementation and Applications (3)

Development of numerical methods for solving linear algebra problems at the heart of many scientific computing problems. Mathematical foundations for the use, implementation and analysis of the algorithms used for solving many optimization problems and differential equations. Prerequisite: MATH 251, MACM 316, programming experience. Quantitative.

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 340 - Algebra II: Rings and Fields (3)

The integers, fundamental theorem of arithmetic. Equivalence relations, modular arithmetic. Univariate polynomials, unique factorization. Rings and fields. Units, zero divisors, integral domains. Ideals, ring homomorphisms. Quotient rings, the ring isomorphism theorem. Chinese remainder theorem. Euclidean, principal ideal, and unique factorization domains. Field extensions, minimal polynomials. Classification of finite fields. Prerequisite: MATH 240 with a minimum grade of C- or MATH 232 with a grade of at least B. Students with credit for MATH 332 may not take this course for further credit. Quantitative.

MATH 343 - Applied Discrete Mathematics (3)

Structures and algorithms, generating elementary combinatorial objects, counting (integer partitions, set partitions, Catalan families), backtracking algorithms, branch and bound, heuristic search algorithms. Prerequisite: MACM 201 (with a grade of at least B-). Recommended: knowledge of a programming language. Quantitative.

MATH 345 - Introduction to Graph Theory (3)

Fundamental concepts, trees and distances, matchings and factors, connectivity and paths, network flows, integral flows. Prerequisite: MACM 201 (with a grade of at least B-). Quantitative.

MATH 348 - Introduction to Probabilistic Models (3)

Review of the basics of probability, including sample space, random variables, expectation and conditioning. Applications of Markov chains, the exponential distribution and the Poisson process from science and industry. Applications may include inventory theory, queuing, forecasting, scheduling and simulation. Prerequisite: STAT 270 and (MATH 232 or MATH 240), all with a minimum grade of C-. Quantitative.

and

MATH 402W - Operations Research Clinic (4)

Problems from operations research will be presented and discussed in class. Students will also work on a problem of their choice and present their solution in report form as well as a presentation. Prerequisite: MATH 308 and STAT 285, with a minimum grade of C-. Writing/Quantitative.

and one additional 400-level MATH course

Statistics

Students complete one of

ECON 333 - Statistical Analysis of Economic Data (4)

An introduction to the use and interpretation of statistical analysis in the context of data typical of economic applications. Prerequisite: ECON 103 or 200, ECON 105 or 205, ECON 233 or BUS (or BUEC) 232 or STAT 270, MATH 157, all with a minimum grade of C-; 60 units. Students with a minimum grade of A- in ECON 233, BUS (or BUEC) 232 or STAT 270 can take ECON 333 after 30 units. Students seeking permission to enroll based on their ECON 233, BUS (or BUEC) 232 or STAT 270 grade must contact the Undergraduate Advisor in Economics. Students with credit for BUEC 333 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Vasyl Golovetskyy
Tu 2:30 PM – 4:20 PM
Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
D101 Tu 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D102 We 9:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D103 We 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D104 We 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D105 We 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D106 Th 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D108 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D109 Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D110 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
STAT 302 - Analysis of Experimental and Observational Data (3)

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in observational and experimental studies. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, or BUEC 232, with a minimum grade of C-. Quantitative.

Section Instructor Day/Time Location
D100 Rachel Altman
Mo 2:30 PM – 3:20 PM
Th 2:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
OP01 TBD
STAT 305 - Introduction to Biostatistical Methods for Health Sciences (3)

Intermediate statistical techniques for the health sciences. Review of introductory concepts in statistics and probability including hypothesis testing, estimation and confidence intervals for means and proportions. Contingency tables and the analysis of multiple 2x2 tables. Correlation and regression. Multiple regression and model selection. Logistic regression and odds ratios. Basic concepts in survival analysis. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, or BUEC 232, with a minimum grade of C-. Quantitative.

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.

and both of

STAT 403 - Intermediate Sampling and Experimental Design (3)

A practical introduction to useful sampling techniques and intermediate level experimental designs. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: STAT 302, 305 or 350 or BUEC 333, all with a minimum grade of C-. Students with credit for STAT 410 or 430 may not take STAT 403 for further credit. 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 BUEC 333 or equivalent, with a minimum grade of C-. Quantitative.

and one of

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 BUEC 333 or equivalent, 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 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.

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 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.

Open Concentration Requirements

Lower Division Requirements

Students complete a minimum of 58 units.

Business Administration

Students complete all of

BUS 200 - Business Fundamentals (3)

Explore the fundamentals of modern business and organizational management. Working with case studies, students will build upon the basics of revenue, profits, contribution and costs, as well as integrate advanced aspects of business models, innovation, competitive advantage, core competence, and strategic analysis. Students with credit for BUS 130 or 201 may not receive further credit for this course. Breadth-Social Sciences.

Section Instructor Day/Time Location
D200 Sasha Ramnarine
Mo 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
E100 Tracy Yiu
We 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
BUS 217W - Critical Thinking in Business (3)

Examine and review today's global economy through critical analysis of differing perspectives. Develop and improve critical thinking and communication skills appropriate to the business environment. Prerequisite: BUS 201 with a minimum grade of C- and 15 units; OR 45 units and corequisite: BUS 202; OR Business Administration joint major, joint honours, or double degree students with 45 units; OR Data Science majors with 15 units. Writing.

Section Instructor Day/Time Location
D100 Susan Christie-Bell
Tu 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D200 Luana Carcano
We 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D300 Luana Carcano
Th 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D400 Jane McCarthy
Fr 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D500 Brent Amburgey
Tu 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
E100 Darelle Odo
Th 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E200 Brent Amburgey
We 4:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
BUS 251 - Financial Accounting I (3)

An introduction to financial accounting, including accounting terminology, understanding financial statements, analysis of a business entity using financial statements. Includes also time value of money and a critical review of the conventional accounting system. Prerequisite: 12 units. Quantitative.

Section Instructor Day/Time Location
D100 Susan Bubra
Mo 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D101 Mo 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D102 Mo 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D103 Mo 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D104 Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D105 Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D200 Susan Bubra
Mo 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D201 Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D202 Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D203 Mo 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D204 Mo 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D205 Mo 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
BUS 272 - Behaviour in Organizations (3)

Theories, concepts and issues in the field of organizational behaviour with an emphasis on individual and team processes. Core topics include employee motivation and performance, stress management, communication, work perceptions and attitudes, decision-making, team dynamics, employee involvement and conflict management. Prerequisite: 12 units.

Section Instructor Day/Time Location
D100 Bahareh Assadi
Th 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D101 Th 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D102 Th 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D103 Th 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D104 Th 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D105 Th 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D106 Th 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D107 Th 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
E100 Sam Thiara
Tu 4:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby
E101 Tu 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
E102 Tu 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
E103 Tu 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
E104 Tu 7:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E105 Tu 7:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E106 Tu 8:30 PM – 9:20 PM
REMOTE LEARNING, Burnaby

Computing Science

Students complete all 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 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 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 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
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: fundamental algorithms; elements of empirical and theoretical algorithmics; abstract data types and elementary data structures; basic object-oriented 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
Toby Donaldson
Mo, We, Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
CMPT 127 - Computing Laboratory (3)

Builds on CMPT 120 to give a hands-on introduction to programming in C and C++, the basics of program design, essential algorithms and data structures. Guided labs teach the standard tools and students exploit these ideas to create software that works. To be taken in parallel with CMPT 125. Prerequisite: CMPT 120 or CMPT 128 or CMPT 130, with a minimum grade of C-. Corequisite: CMPT 125.

Section Instructor Day/Time Location
D100 Alice Yue
Tu 8:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D200 Alice Yue
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D300 Alice Yue
Tu 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
CMPT 225 - Data Structures and Programming (3)

Introduction to a variety of practical and important data structures and methods for implementation and for experimental and analytical evaluation. Topics include: stacks, queues and lists; search trees; hash tables and algorithms; efficient sorting; object-oriented programming; time and space efficiency analysis; and experimental evaluation. Prerequisite: (MACM 101 and ((CMPT 125 and 127), CMPT 129 or CMPT 135)) or (ENSC 251 and ENSC 252), all with a minimum grade of C-. Quantitative.

Section Instructor Day/Time Location
D100 John Edgar
Mo, We, Fr 9:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D101 John Edgar
We 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D102 John Edgar
We 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D103 John Edgar
We 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D104 John Edgar
We 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D105 John Edgar
We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D106 John Edgar
We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D107 John Edgar
We 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D108 John Edgar
We 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
CMPT 276 - Introduction to Software Engineering (3)

An overview of various techniques used for software development and software project management. Major tasks and phases in modern software development, including requirements, analysis, documentation, design, implementation, testing,and maintenance. Project management issues are also introduced. Students complete a team project using an iterative development process. Prerequisite: One W course, CMPT 225, (MACM 101 or (ENSC 251 and ENSC 252)) and (MATH 151 or MATH 150), all with a minimum grade of C-. MATH 154 or MATH 157 with at least a B+ may be substituted for MATH 151 or MATH 150. Students with credit for CMPT 275 may not take this course for further credit.

Section Instructor Day/Time Location
D100 Bobby Chan
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D200 Herbert Tsang
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
CMPT 295 - Introduction to Computer Systems (3)

The curriculum introduces students to topics in computer architecture that are considered fundamental to an understanding of the digital systems underpinnings of computer systems. Prerequisite: Either (MACM 101 and ((CMPT 125 and CMPT 127) or CMPT 135)) or (MATH 151 and CMPT 102 for students in an Applied Physics program), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Arrvindh Shriraman
Mo 12:30 PM – 1:20 PM
Th 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
D101 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D102 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D103 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D104 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D105 Arrvindh Shriraman
Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D106 Arrvindh Shriraman
Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby

Mathematics and Computing Science

Students complete both of

MACM 101 - Discrete Mathematics I (3)

Introduction to counting, induction, automata theory, formal reasoning, modular arithmetic. Prerequisite: BC Math 12 (or equivalent), or any of MATH 100, 150, 151, 154, 157. Quantitative/Breadth-Science.

MACM 201 - Discrete Mathematics II (3)

A continuation of MACM 101. Topics covered include graph theory, trees, inclusion-exclusion, generating functions, recurrence relations, and optimization and matching. Prerequisite: MACM 101 or (ENSC 251 and one of MATH 232 or MATH 240). Quantitative.

Data Science

Students complete

DATA 180 - Undergraduate Seminar in Data Science (1)

A seminar primarily for students undertaking a major or an honours program in Data Science. Prerequisite: Major or honours in Data Science or permission of the program director. Students with credit for DATA (or MSSC) 480 cannot receive credit for DATA (or MSSC) 180.

Mathematics

Students complete 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 Seyyed Aliasghar Hosseini
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, 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 - Calculus I for the Biological Sciences (3)

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: 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 Justin Chan
Mo, We, Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
OP01 TBD

and both 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 Vijaykumar Singh
Mo, We, Fr 8:30 AM – 9:20 AM
REMOTE LEARNING, Burnaby
OP01 TBD
MATH 208W - Introduction to Operations Research (3)

Introduction to methods of operations research: linear and nonlinear programming, simulation, and heuristic methods. Applications to transportation, assignment, scheduling, and game theory. Exposure to mathematical models of industry and technology. Emphasis on computation for analysis and simulation. Prerequisite: MATH 150 or 151 or 154 or 157, with a minimum grade of C-. Students with credit for MATH 208 may not take this course for further credit. Writing/Quantitative.

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
D200 Justin Chan
Mo, We, Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
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 Shuxing Li
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

Statistics

Students complete all of

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, 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.

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, 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, 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.

and one of

BUS 232 - Data and Decisions I (4)

An introduction to business statistics with a heavy emphasis on applications and the use of EXCEL. Students will be required to use statistical applications to solve business problems. Prerequisite: MATH 150, MATH 151, MATH 154, or MATH 157, with a minimum grade of C-; 15 units. MATH 150, MATH 151, MATH 154, or MATH 157 may be taken concurrently with BUS 232. Students with credit for BUEC 232 or ECON 233 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Andrew Flostrand
Tu, Th 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D200 Cleusa Yamamoto
Mo 1:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
OP01 Tu 12:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
OP02 Th 12:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
OP03 Mo 9:30 AM – 1:20 PM
REMOTE LEARNING, Burnaby
OP04 Mo 5:30 PM – 9:20 PM
REMOTE LEARNING, Burnaby
OP05 TBD
OP06 TBD
STAT 201 - Statistics for the Life Sciences (3)

Research methodology and associated statistical analysis techniques for students with training in the life sciences. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units. Students cannot obtain credit for STAT 201 if they already have credit for - or are taking concurrently - STAT 101, 203, 205, 285, or any upper division STAT course. Quantitative.

Section Instructor Day/Time Location
D100 Tim Swartz
We 1:30 PM – 2:20 PM
Fr 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
OP01 TBD
STAT 203 - Introduction to Statistics for the Social Sciences (3)

Descriptive and inferential statistics aimed at students in the social sciences. Scales of measurement. Descriptive statistics. Measures of association. Hypothesis tests and confidence intervals. Students in Sociology and Anthropology are expected to take SA 255 before this course. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units including a research methods course such as SA 255, CRIM 220, POL 200, or equivalent. Students cannot obtain credit for STAT 203 if they already have credit for - or are taking concurrently - STAT 101, 201, 205, 285, or any upper division STAT course. Quantitative.

Section Instructor Day/Time Location
E100 Scott Pai
Tu 4:30 PM – 5:20 PM
Th 4:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
OP01 TBD
STAT 205 - Introduction to Statistics (3)

The collection, description, analysis and summary of data, including the concepts of frequency distribution, parameter estimation and hypothesis testing. Intended to be particularly accessible to students who are not specializing in Statistics. Prerequisite: Recommended: 30 units. Students cannot obtain credit for STAT 205 if they already have credit for - or are taking concurrently - STAT 101, 201, 203, 285, or any upper division STAT course. Quantitative.

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 Scott Pai
We 11:30 AM – 12:20 PM
Fr 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
OP01 TBD

* Recommended

Upper Division Requirements

Students complete a minimum of 52 units.

Business Administration

Students complete all of

BUS 343 - Introduction to Marketing (3)

The environment of marketing; relation of social sciences to marketing; evaluation of marketing theory and research; assessment of demand, consumer behaviour analysis; market institutions; method and mechanics of distribution in domestic, foreign and overseas markets; sales organization; advertising; new product development, publicity and promotion; marketing programs. Prerequisite: 45 units. Students with credit for COMM 343 may not take this course for further credit.

Section Instructor Day/Time Location
D100 Pei-Shiuan Lin
Fr 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D101 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D102 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D103 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D104 Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D105 Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D106 Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D107 Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D108 Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D109 Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D200 Pei-Shiuan Lin
Fr 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D201 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D202 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D203 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D204 Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D205 Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D206 Fr 11:30 AM – 12:30 PM
REMOTE LEARNING, Burnaby
D207 Fr 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D208 Fr 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D209 Fr 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
BUS 360W - Business Communication (4)

This course is designed to assist students to improve their written and oral communication skills in business settings. The theory and practice of business communication will be presented. Topics include analysis of communication problems, message character, message monitoring, message media. Exercises in individual and group messages and presentations will be conducted. Prerequisite: This course is open to students admitted prior to Fall 2014 to the Business Administration major, honours, or second degree program and who have 45 units, OR to students admitted Fall 2014 - Summer 2017 to the Business Administration major, honours, or second degree program and who have 45 units and BUS 130 or 201 or 202 or 301, with a minimum grade of C-, OR to student admitted Fall 2017 - onwards to the Business Administration major, honours, or second degree program and who have 45 units and BUS 130 or 201 or 202 or 301, with a minimum grade of C- and BUS 217W with a minimum grade of C-, OR to Business Administration joint major or joint honours students with BUS 217W with a minimum grade of C- and 45 units, OR to Business and Economics Joint Major students with ECON 220W with a minimum grade of C- and 45 units, OR to Mechatronic Systems Engineering and Business Administration double degree students with 45 units, OR to Management Systems Science or Actuarial Science majors with 45 units OR to Data Science major with BUS 217W with a minimum grade of C- and 45 units. Students who have taken BUS 360 may not take this course for further credit. Writing.

Section Instructor Day/Time Location
D100 Leanne Barlow
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D200 Leanne Barlow
Th 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D300 Christian Venhuizen
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D400 Christian Venhuizen
We 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D500 Susan Christie-Bell
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
E100 Eric Tung
We 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E200 Eric Tung
Th 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
BUS 439 - Analytics Project (3) *

Examines complex, real-world decision making issues using an evidence-based approach that employs decision making strategies involving statistics, data management, analytics, and decision theory. Through a major decision making project within the community, students will experience first-hand the process of consultation, data acquisition, analysis, and recommendation. The data in the project will be proprietary to the community partners and students thus need to sign a non-disclosure agreement. A non-disclosure agreement template is attached to the course outline. The results of the project will remain the intellectual property of the students; notwithstanding, those results will be shared with the data provider. Students also have an option to complete a project with non-proprietary data. Prerequisite: BUS 345 or BUS 440, BUS 360W, BUS 437 or BUS 441, BUS 445, BUS 462, and BUS 464, all with a minimum grade of C-; 90 units; OR Data Science majors with BUS 360W, BUS 445, CMPT 354, all with a minimum grade of C- and 90 units.

BUS 445 - Customer Analytics (3) **

Exposes students to the art of using analytic tools from across the spectrum of data mining and modeling to provide powerful competitive advantage in business. Students will learn to recognize when a method should or should not be used, what data is required, and how to use the software tools. Areas covered include database marketing, geospatial marketing and fundamental strategic and tactical decisions such as segmentation, targeting and allocating resources to the marketing mix. Prerequisite: BUS 343, 336, 360W, all with a minimum grade of C-, 60 units; OR Data Science majors with BUS 343, 360W, both with a minimum grade of C-, and 60 units.

Section Instructor Day/Time Location
D100 Yupin Yang
We 9:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby

*For this course, Data Science students are eligible for a prerequisite waiver for BUS 345, 437, 445, 462, 464, 90 units. Students should consult with their program advisor.

**For this course, Data Science students are eligible for a prerequisite waiver for BUS 336. Students should consult with their program advisor.

Computing Science

Students complete all of

CMPT 300 - Operating Systems I (3)

This course aims to give the student an understanding of what a modern operating system is, and the services it provides. It also discusses some basic issues in operating systems and provides solutions. Topics include multiprogramming, process management, memory management, and file systems. Prerequisite: CMPT 225 and (CMPT 295 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Harinder Khangura
Tu 12:30 PM – 2:20 PM
Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 307 - Data Structures and Algorithms (3)

Analysis and design of data structures for lists, sets, trees, dictionaries, and priority queues. A selection of topics chosen from sorting, memory management, graphs and graph algorithms. Prerequisite: CMPT 225, MACM 201, MATH 151 (or MATH 150), and MATH 232 or 240, all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 David Mitchell
Mo, We, Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
CMPT 353 - Computational Data Science (3)

Basic concepts and programming tools for handling and processing data. Includes data acquisition, cleaning data sources, application of machine learning techniques and data analysis techniques, large-scale computation on a computing cluster. Prerequisite: CMPT 225 and (STAT 101, STAT 270, ENSC 280, or MSE 210), with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Gregory Baker
Tu 10:30 AM – 12:20 PM
Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 354 - Database Systems I (3)

Logical representations of data records. Data models. Studies of some popular file and database systems. Document retrieval. Other related issues such as database administration, data dictionary and security. Prerequisite: CMPT 225 and (MACM 101 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Jack Thomas
Mo 8:30 AM – 9:20 AM
Th 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 454 - Database Systems II (3)

An advanced course on database systems which covers crash recovery, concurrency control, transaction processing, distributed database systems as the core material and a set of selected topics based on the new developments and research interests, such as object-oriented data models and systems, extended relational systems, deductive database systems, and security and integrity. Prerequisite: CMPT 300 and 354, with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Ouldooz Baghban Karimi
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby

Mathematics

Students complete one of

MATH 308 - Linear Optimization (3)

Linear programming modelling. The simplex method and its variants. Duality theory. Post-optimality analysis. Applications and software. Additional topics may include: game theory, network simplex algorithm, and convex sets. Prerequisite: MATH 150, 151, 154, or 157 and MATH 240 or 232, all with a minimum grade of C-. Quantitative.

MATH 309 - Continuous Optimization (3)

Theoretical and computational methods for investigating the minimum of a function of several real variables with and without inequality constraints. Applications to operations research, model fitting, and economic theory. Prerequisite: MATH 232 or 240, and 251, all with a minimum grade of C-. Quantitative.

Statistics

Students complete one of

ECON 333 - Statistical Analysis of Economic Data (4)

An introduction to the use and interpretation of statistical analysis in the context of data typical of economic applications. Prerequisite: ECON 103 or 200, ECON 105 or 205, ECON 233 or BUS (or BUEC) 232 or STAT 270, MATH 157, all with a minimum grade of C-; 60 units. Students with a minimum grade of A- in ECON 233, BUS (or BUEC) 232 or STAT 270 can take ECON 333 after 30 units. Students seeking permission to enroll based on their ECON 233, BUS (or BUEC) 232 or STAT 270 grade must contact the Undergraduate Advisor in Economics. Students with credit for BUEC 333 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Vasyl Golovetskyy
Tu 2:30 PM – 4:20 PM
Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
D101 Tu 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D102 We 9:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D103 We 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D104 We 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D105 We 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D106 Th 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D108 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D109 Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D110 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
STAT 302 - Analysis of Experimental and Observational Data (3)

The standard techniques of multiple regression analysis, analysis of variance, and analysis of covariance, and their role in observational and experimental studies. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, or BUEC 232, with a minimum grade of C-. Quantitative.

Section Instructor Day/Time Location
D100 Rachel Altman
Mo 2:30 PM – 3:20 PM
Th 2:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
OP01 TBD
STAT 305 - Introduction to Biostatistical Methods for Health Sciences (3)

Intermediate statistical techniques for the health sciences. Review of introductory concepts in statistics and probability including hypothesis testing, estimation and confidence intervals for means and proportions. Contingency tables and the analysis of multiple 2x2 tables. Correlation and regression. Multiple regression and model selection. Logistic regression and odds ratios. Basic concepts in survival analysis. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, or BUEC 232, with a minimum grade of C-. Quantitative.

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.

and both of

STAT 403 - Intermediate Sampling and Experimental Design (3)

A practical introduction to useful sampling techniques and intermediate level experimental designs. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: STAT 302, 305 or 350 or BUEC 333, all with a minimum grade of C-. Students with credit for STAT 410 or 430 may not take STAT 403 for further credit. 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 BUEC 333 or equivalent, with a minimum grade of C-. Quantitative.

and one of

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 BUEC 333 or equivalent, 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 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.

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 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.

Students must complete 9 additional units from this list

BUS 345 - Marketing Research (4)

A course in the management of marketing research. The basics of the design, conduct, and analysis of marketing research studies. Prerequisite: BUS 343, 336, both with a minimum grade of C-; 45 units; OR Data Science majors with BUS 343 with a minimum grade of C- and 45 units. Students with credit for BUS 442 may not complete this course for further credit.

Section Instructor Day/Time Location
D100 Emily Treen
Fr 9:30 AM – 1:20 PM
REMOTE LEARNING, Burnaby
D200 Mitsu Feng
Mo 1:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
BUS 362 - Business Process Analysis (4)

Prepares students to model, analyze and propose improvements to business processes. In the major project, students analyze a process within an organization and use current techniques and tools to propose changes and a supporting information system. Prerequisite: BUS 237 with a minimum grade of C-; 45 units; OR Data Science majors with 45 units. Students with credit for BUS 394 may not take this course for further credit.

Section Instructor Day/Time Location
D100 Drew Parker
Th 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D101 Th 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D102 Th 2:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D103 Th 4:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby
BUS 437 - Decision Analysis in Business (3)

A seminar in the use of Bayesian techniques in business decisions. Prerequisite: BUS 336, 360W, both with a minimum grade of C-; 60 units; OR Data Science majors with BUS 360W with a minimum grade of C- and 60 units.

Section Instructor Day/Time Location
D100 Payman Jula
Fr 9:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
BUS 440 - Simulation in Management Decision-making (4)

Development and use of simulation models as an aid in making complex management decisions. Hands on use of business related tools for computer simulation. Issues related to design and validation of simulation models, the assessment of input data, and the interpretation and use of simulation output. Prerequisite: BUS 336, 360W, both with a minimum grade of C-, 60 units; OR Data Science majors with BUS 360W with a minimum grade of C-, 60 units.

CMPT 308 - Computability and Complexity (3)

This course introduces students to formal models of computations such as Turing machines and RAMs. Notions of tractability and intractability are discusses both with respect to computability and resource requirements. The relationship of these concepts to logic is also covered. Prerequisite: MACM 201 with a minimum grade of C-.

CMPT 310 - Artificial Intelligence Survey (3)

Provides a unified discussion of the fundamental approaches to the problems in artificial intelligence. The topics considered are: representational typology and search methods; game playing, heuristic programming; pattern recognition and classification; theorem-proving; question-answering systems; natural language understanding; computer vision. Prerequisite: CMPT 225 and (MACM 101 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-. Students with credit for CMPT 410 may not take this course for further credit.

Section Instructor Day/Time Location
D100 Toby Donaldson
Milan Tofiloski
We 9:30 AM – 10:20 AM
Fr 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 322W - Professional Responsibility and Ethics (3)

The theory and practice of computer ethics. The basis for ethical decision-making and the methodology for reaching ethical decisions concerning computing matters will be studied. Writing as a means to understand and reason about complex ethical issues will be emphasized. Prerequisite: Three CMPT units, 30 total units, and any lower division W course. Students with credit for CMPT 322 may not take this course for further credit. Writing.

CMPT 373 - Software Development Methods (3)

Survey of modern software development methodology. Several software development process models will be examined, as will the general principles behind such models. Provides experience with different programming paradigms and their advantages and disadvantages during software development. Prerequisite: CMPT 276 or 275, with a minimum grade of C-.

CMPT 376W - Technical Writing and Group Dynamics (3)

Covers professional writing in computing science, including format conventions and technical reports. Attention is paid to group dynamics, including team leadership, dispute resolution, cognitive bias, professional ethics and collaborative writing. Research methods are also discussed. The use of LaTeX and various version control tools are emphasized. Prerequisite: CMPT 105W and (CMPT 275 or CMPT 276), with a minimum grade of C-. Students with credit for CMPT 376 may not take this course for further credit. Writing.

Section Instructor Day/Time Location
D200 Steve Pearce
Mo, We, Fr 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D400 Jacqueline Nelsen
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
CMPT 405 - Design and Analysis of Computing Algorithms (3)

Models of computation, methods of algorithm design; complexity of algorithms; algorithms on graphs, NP-completeness, approximation algorithms, selected topics. Prerequisite: CMPT 307 with a minimum grade of C-.

CMPT 417 - Intelligent Systems (3)

Intelligent Systems using modern constraint programming and heuristic search methods. A survey of this rapidly advancing technology as applied to scheduling, planning, design and configuration. An introduction to constraint programming, heuristic search, constructive (backtrack) search, iterative improvement (local) search, mixed-initiative systems and combinatorial optimization. Prerequisite: CMPT 225 with a minimum grade of C-.

CMPT 419 - Special Topics in Artificial Intelligence (3)

Current topics in artificial intelligence depending on faculty and student interest.

CMPT 470 - Web-based Information Systems (3)

This course examines: two-tier/multi-tier client/server architectures; the architecture of a Web-based information system; web servers/browser; programming/scripting tools for clients and servers; database access; transport of programming objects; messaging systems; security; and applications (such as e-commerce and on-line learning). Prerequisite: (CMPT 275 or CMPT 276) and CMPT 354, with a minimum grade of C-.

Section Instructor Day/Time Location
E100 Bobby Chan
Th 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
MACM 316 - Numerical Analysis I (3)

A presentation of the problems commonly arising in numerical analysis and scientific computing and the basic methods for their solutions. Prerequisite: MATH 152 or 155 or 158, and MATH 232 or 240, and computing experience. Quantitative.

Section Instructor Day/Time Location
D100 Pengyu Liu
Mo, We, Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D101 Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D102 Mo 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D103 Tu 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D104 Tu 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D105 Tu 9:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D106 Tu 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D107 Mo 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D108 Mo 5:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby
MATH 343 - Applied Discrete Mathematics (3)

Structures and algorithms, generating elementary combinatorial objects, counting (integer partitions, set partitions, Catalan families), backtracking algorithms, branch and bound, heuristic search algorithms. Prerequisite: MACM 201 (with a grade of at least B-). Recommended: knowledge of a programming language. Quantitative.

MATH 345 - Introduction to Graph Theory (3)

Fundamental concepts, trees and distances, matchings and factors, connectivity and paths, network flows, integral flows. Prerequisite: MACM 201 (with a grade of at least B-). 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 BUEC 333, with a minimum grade of C-. Students with credit for STAT 340 may not take STAT 342 for further credit.

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 BUEC 333 or equivalent, 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 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.

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 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.

Statistics Concentration Requirements

Lower Division Requirements

Students complete a minimum of 64 units.

Business Administration

Students complete all of

BUS 200 - Business Fundamentals (3)

Explore the fundamentals of modern business and organizational management. Working with case studies, students will build upon the basics of revenue, profits, contribution and costs, as well as integrate advanced aspects of business models, innovation, competitive advantage, core competence, and strategic analysis. Students with credit for BUS 130 or 201 may not receive further credit for this course. Breadth-Social Sciences.

Section Instructor Day/Time Location
D200 Sasha Ramnarine
Mo 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
E100 Tracy Yiu
We 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
BUS 217W - Critical Thinking in Business (3)

Examine and review today's global economy through critical analysis of differing perspectives. Develop and improve critical thinking and communication skills appropriate to the business environment. Prerequisite: BUS 201 with a minimum grade of C- and 15 units; OR 45 units and corequisite: BUS 202; OR Business Administration joint major, joint honours, or double degree students with 45 units; OR Data Science majors with 15 units. Writing.

Section Instructor Day/Time Location
D100 Susan Christie-Bell
Tu 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D200 Luana Carcano
We 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D300 Luana Carcano
Th 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D400 Jane McCarthy
Fr 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D500 Brent Amburgey
Tu 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
E100 Darelle Odo
Th 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E200 Brent Amburgey
We 4:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
BUS 251 - Financial Accounting I (3)

An introduction to financial accounting, including accounting terminology, understanding financial statements, analysis of a business entity using financial statements. Includes also time value of money and a critical review of the conventional accounting system. Prerequisite: 12 units. Quantitative.

Section Instructor Day/Time Location
D100 Susan Bubra
Mo 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D101 Mo 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D102 Mo 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D103 Mo 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D104 Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D105 Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D200 Susan Bubra
Mo 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D201 Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D202 Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D203 Mo 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D204 Mo 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D205 Mo 4:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
BUS 272 - Behaviour in Organizations (3)

Theories, concepts and issues in the field of organizational behaviour with an emphasis on individual and team processes. Core topics include employee motivation and performance, stress management, communication, work perceptions and attitudes, decision-making, team dynamics, employee involvement and conflict management. Prerequisite: 12 units.

Section Instructor Day/Time Location
D100 Bahareh Assadi
Th 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D101 Th 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D102 Th 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D103 Th 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D104 Th 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D105 Th 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D106 Th 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D107 Th 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
E100 Sam Thiara
Tu 4:30 PM – 6:20 PM
REMOTE LEARNING, Burnaby
E101 Tu 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
E102 Tu 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
E103 Tu 6:30 PM – 7:20 PM
REMOTE LEARNING, Burnaby
E104 Tu 7:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E105 Tu 7:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E106 Tu 8:30 PM – 9:20 PM
REMOTE LEARNING, Burnaby

Computing Science

Students complete all 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 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 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 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
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: fundamental algorithms; elements of empirical and theoretical algorithmics; abstract data types and elementary data structures; basic object-oriented 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
Toby Donaldson
Mo, We, Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
CMPT 127 - Computing Laboratory (3)

Builds on CMPT 120 to give a hands-on introduction to programming in C and C++, the basics of program design, essential algorithms and data structures. Guided labs teach the standard tools and students exploit these ideas to create software that works. To be taken in parallel with CMPT 125. Prerequisite: CMPT 120 or CMPT 128 or CMPT 130, with a minimum grade of C-. Corequisite: CMPT 125.

Section Instructor Day/Time Location
D100 Alice Yue
Tu 8:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D200 Alice Yue
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D300 Alice Yue
Tu 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
CMPT 225 - Data Structures and Programming (3)

Introduction to a variety of practical and important data structures and methods for implementation and for experimental and analytical evaluation. Topics include: stacks, queues and lists; search trees; hash tables and algorithms; efficient sorting; object-oriented programming; time and space efficiency analysis; and experimental evaluation. Prerequisite: (MACM 101 and ((CMPT 125 and 127), CMPT 129 or CMPT 135)) or (ENSC 251 and ENSC 252), all with a minimum grade of C-. Quantitative.

Section Instructor Day/Time Location
D100 John Edgar
Mo, We, Fr 9:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D101 John Edgar
We 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D102 John Edgar
We 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D103 John Edgar
We 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D104 John Edgar
We 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D105 John Edgar
We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D106 John Edgar
We 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D107 John Edgar
We 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D108 John Edgar
We 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
CMPT 276 - Introduction to Software Engineering (3)

An overview of various techniques used for software development and software project management. Major tasks and phases in modern software development, including requirements, analysis, documentation, design, implementation, testing,and maintenance. Project management issues are also introduced. Students complete a team project using an iterative development process. Prerequisite: One W course, CMPT 225, (MACM 101 or (ENSC 251 and ENSC 252)) and (MATH 151 or MATH 150), all with a minimum grade of C-. MATH 154 or MATH 157 with at least a B+ may be substituted for MATH 151 or MATH 150. Students with credit for CMPT 275 may not take this course for further credit.

Section Instructor Day/Time Location
D100 Bobby Chan
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D200 Herbert Tsang
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
CMPT 295 - Introduction to Computer Systems (3)

The curriculum introduces students to topics in computer architecture that are considered fundamental to an understanding of the digital systems underpinnings of computer systems. Prerequisite: Either (MACM 101 and ((CMPT 125 and CMPT 127) or CMPT 135)) or (MATH 151 and CMPT 102 for students in an Applied Physics program), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Arrvindh Shriraman
Mo 12:30 PM – 1:20 PM
Th 12:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
D101 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D102 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D103 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D104 Arrvindh Shriraman
Mo 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D105 Arrvindh Shriraman
Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D106 Arrvindh Shriraman
Mo 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby

Mathematics and Computing Science

Students complete both of

MACM 101 - Discrete Mathematics I (3)

Introduction to counting, induction, automata theory, formal reasoning, modular arithmetic. Prerequisite: BC Math 12 (or equivalent), or any of MATH 100, 150, 151, 154, 157. Quantitative/Breadth-Science.

MACM 201 - Discrete Mathematics II (3)

A continuation of MACM 101. Topics covered include graph theory, trees, inclusion-exclusion, generating functions, recurrence relations, and optimization and matching. Prerequisite: MACM 101 or (ENSC 251 and one of MATH 232 or MATH 240). Quantitative.

Data Science

Students complete

DATA 180 - Undergraduate Seminar in Data Science (1)

A seminar primarily for students undertaking a major or an honours program in Data Science. Prerequisite: Major or honours in Data Science or permission of the program director. Students with credit for DATA (or MSSC) 480 cannot receive credit for DATA (or MSSC) 180.

Mathematics

Students complete 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 Seyyed Aliasghar Hosseini
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, 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 - Calculus I for the Biological Sciences (3)

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: 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 Justin Chan
Mo, We, Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
OP01 TBD

and all 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 Vijaykumar Singh
Mo, We, Fr 8:30 AM – 9:20 AM
REMOTE LEARNING, Burnaby
OP01 TBD
MATH 208W - Introduction to Operations Research (3)

Introduction to methods of operations research: linear and nonlinear programming, simulation, and heuristic methods. Applications to transportation, assignment, scheduling, and game theory. Exposure to mathematical models of industry and technology. Emphasis on computation for analysis and simulation. Prerequisite: MATH 150 or 151 or 154 or 157, with a minimum grade of C-. Students with credit for MATH 208 may not take this course for further credit. Writing/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 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

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
D200 Justin Chan
Mo, We, Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
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 Shuxing Li
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

Statistics

Students complete all of

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, 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.

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, 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, 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 Scott Pai
We 11:30 AM – 12:20 PM
Fr 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
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.

* Recommended

Upper Division Requirements

Students complete a minimum of 49 units.

Business Administration

Students complete all of

BUS 343 - Introduction to Marketing (3)

The environment of marketing; relation of social sciences to marketing; evaluation of marketing theory and research; assessment of demand, consumer behaviour analysis; market institutions; method and mechanics of distribution in domestic, foreign and overseas markets; sales organization; advertising; new product development, publicity and promotion; marketing programs. Prerequisite: 45 units. Students with credit for COMM 343 may not take this course for further credit.

Section Instructor Day/Time Location
D100 Pei-Shiuan Lin
Fr 10:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D101 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D102 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D103 Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
D104 Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D105 Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D106 Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby
D107 Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D108 Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D109 Fr 2:30 PM – 3:20 PM
REMOTE LEARNING, Burnaby
D200 Pei-Shiuan Lin
Fr 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
D201 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D202 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D203 Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
D204 Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D205 Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
D206 Fr 11:30 AM – 12:30 PM
REMOTE LEARNING, Burnaby
D207 Fr 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D208 Fr 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
D209 Fr 3:30 PM – 4:20 PM
REMOTE LEARNING, Burnaby
BUS 360W - Business Communication (4)

This course is designed to assist students to improve their written and oral communication skills in business settings. The theory and practice of business communication will be presented. Topics include analysis of communication problems, message character, message monitoring, message media. Exercises in individual and group messages and presentations will be conducted. Prerequisite: This course is open to students admitted prior to Fall 2014 to the Business Administration major, honours, or second degree program and who have 45 units, OR to students admitted Fall 2014 - Summer 2017 to the Business Administration major, honours, or second degree program and who have 45 units and BUS 130 or 201 or 202 or 301, with a minimum grade of C-, OR to student admitted Fall 2017 - onwards to the Business Administration major, honours, or second degree program and who have 45 units and BUS 130 or 201 or 202 or 301, with a minimum grade of C- and BUS 217W with a minimum grade of C-, OR to Business Administration joint major or joint honours students with BUS 217W with a minimum grade of C- and 45 units, OR to Business and Economics Joint Major students with ECON 220W with a minimum grade of C- and 45 units, OR to Mechatronic Systems Engineering and Business Administration double degree students with 45 units, OR to Management Systems Science or Actuarial Science majors with 45 units OR to Data Science major with BUS 217W with a minimum grade of C- and 45 units. Students who have taken BUS 360 may not take this course for further credit. Writing.

Section Instructor Day/Time Location
D100 Leanne Barlow
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D200 Leanne Barlow
Th 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D300 Christian Venhuizen
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
D400 Christian Venhuizen
We 2:30 PM – 5:20 PM
REMOTE LEARNING, Burnaby
D500 Susan Christie-Bell
Tu 11:30 AM – 2:20 PM
REMOTE LEARNING, Burnaby
E100 Eric Tung
We 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
E200 Eric Tung
Th 5:30 PM – 8:20 PM
REMOTE LEARNING, Burnaby
BUS 439 - Analytics Project (3) *

Examines complex, real-world decision making issues using an evidence-based approach that employs decision making strategies involving statistics, data management, analytics, and decision theory. Through a major decision making project within the community, students will experience first-hand the process of consultation, data acquisition, analysis, and recommendation. The data in the project will be proprietary to the community partners and students thus need to sign a non-disclosure agreement. A non-disclosure agreement template is attached to the course outline. The results of the project will remain the intellectual property of the students; notwithstanding, those results will be shared with the data provider. Students also have an option to complete a project with non-proprietary data. Prerequisite: BUS 345 or BUS 440, BUS 360W, BUS 437 or BUS 441, BUS 445, BUS 462, and BUS 464, all with a minimum grade of C-; 90 units; OR Data Science majors with BUS 360W, BUS 445, CMPT 354, all with a minimum grade of C- and 90 units.

BUS 445 - Customer Analytics (3) **

Exposes students to the art of using analytic tools from across the spectrum of data mining and modeling to provide powerful competitive advantage in business. Students will learn to recognize when a method should or should not be used, what data is required, and how to use the software tools. Areas covered include database marketing, geospatial marketing and fundamental strategic and tactical decisions such as segmentation, targeting and allocating resources to the marketing mix. Prerequisite: BUS 343, 336, 360W, all with a minimum grade of C-, 60 units; OR Data Science majors with BUS 343, 360W, both with a minimum grade of C-, and 60 units.

Section Instructor Day/Time Location
D100 Yupin Yang
We 9:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby

*For this course, Data Science students are eligible for a prerequisite waiver for BUS 345, 437, 445, 462, 464, 90 units. Students should consult with their program advisor.

**For this course, Data Science students are eligible for a prerequisite waiver for BUS 336. Students should consult with their program advisor.

Computing Science

Students complete all of

CMPT 300 - Operating Systems I (3)

This course aims to give the student an understanding of what a modern operating system is, and the services it provides. It also discusses some basic issues in operating systems and provides solutions. Topics include multiprogramming, process management, memory management, and file systems. Prerequisite: CMPT 225 and (CMPT 295 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Harinder Khangura
Tu 12:30 PM – 2:20 PM
Fr 12:30 PM – 1:20 PM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 307 - Data Structures and Algorithms (3)

Analysis and design of data structures for lists, sets, trees, dictionaries, and priority queues. A selection of topics chosen from sorting, memory management, graphs and graph algorithms. Prerequisite: CMPT 225, MACM 201, MATH 151 (or MATH 150), and MATH 232 or 240, all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 David Mitchell
Mo, We, Fr 11:30 AM – 12:20 PM
REMOTE LEARNING, Burnaby
CMPT 353 - Computational Data Science (3)

Basic concepts and programming tools for handling and processing data. Includes data acquisition, cleaning data sources, application of machine learning techniques and data analysis techniques, large-scale computation on a computing cluster. Prerequisite: CMPT 225 and (STAT 101, STAT 270, ENSC 280, or MSE 210), with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Gregory Baker
Tu 10:30 AM – 12:20 PM
Fr 10:30 AM – 11:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 354 - Database Systems I (3)

Logical representations of data records. Data models. Studies of some popular file and database systems. Document retrieval. Other related issues such as database administration, data dictionary and security. Prerequisite: CMPT 225 and (MACM 101 or (ENSC 251 and ENSC 252)), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Jack Thomas
Mo 8:30 AM – 9:20 AM
Th 8:30 AM – 10:20 AM
REMOTE LEARNING, Burnaby
REMOTE LEARNING, Burnaby
CMPT 454 - Database Systems II (3)

An advanced course on database systems which covers crash recovery, concurrency control, transaction processing, distributed database systems as the core material and a set of selected topics based on the new developments and research interests, such as object-oriented data models and systems, extended relational systems, deductive database systems, and security and integrity. Prerequisite: CMPT 300 and 354, with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Ouldooz Baghban Karimi
Mo, We, Fr 1:30 PM – 2:20 PM
REMOTE LEARNING, Burnaby

Mathematics

Students complete one of

MATH 308 - Linear Optimization (3)

Linear programming modelling. The simplex method and its variants. Duality theory. Post-optimality analysis. Applications and software. Additional topics may include: game theory, network simplex algorithm, and convex sets. Prerequisite: MATH 150, 151, 154, or 157 and MATH 240 or 232, all with a minimum grade of C-. Quantitative.

MATH 309 - Continuous Optimization (3)

Theoretical and computational methods for investigating the minimum of a function of several real variables with and without inequality constraints. Applications to operations research, model fitting, and economic theory. Prerequisite: MATH 232 or 240, and 251, all with a minimum grade of C-. Quantitative.

Statistics

Students complete all of

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 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 403 - Intermediate Sampling and Experimental Design (3)

A practical introduction to useful sampling techniques and intermediate level experimental designs. This course may not be used to satisfy the upper division requirements of the Statistics major or honours program. Prerequisite: STAT 302, 305 or 350 or BUEC 333, all with a minimum grade of C-. Students with credit for STAT 410 or 430 may not take STAT 403 for further credit. Quantitative.

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

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
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: B-Soc
6 units Humanities: B-Hum
6 units Sciences: B-Sci

6

Additional Breadth 6 units 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.

Double Majors and Minors

Students wishing to complete a second major or a minor in addition to a Data Science (DATA) major must satisfy all DATA requirements. At least 34 upper division units must be allocated exclusively to the DATA major.

This includes at least nine units from each of the lists under the sub-headings Business Administration, Computing Science, Mathematics and Statistics. Units used to satisfy DATA upper division requirements beyond these 34 can be applied simultaneously to the other major, minor or honours.