Please note:

To view the Fall 2022 Academic Calendar, go to www.sfu.ca/students/calendar/2022/fall.html.

Department of Statistics and Actuarial Science | Faculty of Science Simon Fraser University Calendar | Spring 2023

Data Science Major

Bachelor of Science

The Faculty of Science, with the Departments of Statistics and Actuarial Science and of Mathematics, the Beedie School of Business, and the School of Computing Science, offers a major in Data Science (DATA) leading to a bachelor of science (BSc). 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 program is overseen by the Department of Statistics and Actuarial Science. A steering committee consisting of representatives from the above mentioned departments and faculty serve as liaison 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 2.7 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.

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

Program Requirements

Students complete 120 units, as specified below.

Under program and University regulations, a general degree requires a total of 120 units, 44 of which are in upper division courses. Completion of all lower and upper division courses shown below is required. However, students should be aware of particular department requirements for course entry. Contact those departments for information.

Lower Division Requirements

Students complete a total of 55-57 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. Breadth-Social Sciences.

Section Instructor Day/Time Location
D100 Parveen Karsan
We 2:30 PM – 5:20 PM
AQ 3150, Burnaby
E100 Shannon Wong
We 5:30 PM – 8:20 PM
WMC 3260, 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 major with 15 units. Writing.

Section Instructor Day/Time Location
D100 Luana Carcano
We 2:30 PM – 5:20 PM
BLU 10021, Burnaby
D200 Matthew Martell
Tu 2:30 PM – 5:20 PM
SRYC 5080, Surrey
D300 Matthew Martell
Mo 2:30 PM – 5:20 PM
WMC 2230, Burnaby
D500 Jane McCarthy
Tu 2:30 PM – 5:20 PM
AQ 3153, Burnaby
E100 Michelle Corbett
We 5:30 PM – 8:20 PM
WMC 2230, Burnaby
E200 Jane McCarthy
Mo 4:30 PM – 7:20 PM
AQ 3003, 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 Anne Macdonald
Th 10:30 AM – 12:20 PM
SSCC 9002, Burnaby
D101 Th 12:30 PM – 1:20 PM
AQ 5004, Burnaby
D102 Th 12:30 PM – 1:20 PM
BLU 11911, Burnaby
D103 Th 12:30 PM – 1:20 PM
BLU 11901, Burnaby
D104 Th 1:30 PM – 2:20 PM
WMC 3251, Burnaby
D105 Th 1:30 PM – 2:20 PM
WMC 2268, Burnaby
D106 Th 1:30 PM – 2:20 PM
WMC 2260, Burnaby
D107 Th 2:30 PM – 3:20 PM
WMC 2501, Burnaby
D108 Th 2:30 PM – 3:20 PM
AQ 5014, Burnaby
D200 Praise Ma
Fr 10:30 AM – 12:20 PM
SRYC 2740, Surrey
D201 Fr 12:30 PM – 1:20 PM
SRYC 2990, Surrey
D202 Fr 12:30 PM – 1:20 PM
SRYC 2995, Surrey
D203 Fr 1:30 PM – 2:20 PM
SRYC 2990, Surrey
D204 Fr 1:30 PM – 2:20 PM
SRYC 2995, Surrey
E100 Anne Macdonald
We 5:30 PM – 7:20 PM
EDB 7618, Burnaby
E101 We 7:30 PM – 8:20 PM
SWH 10061, Burnaby
E102 We 7:30 PM – 8:20 PM
SWH 10051, Burnaby
E103 We 7:30 PM – 8:20 PM
SWH 10075, Burnaby
E104 We 8:30 PM – 9:20 PM
SWH 10051, Burnaby
E105 We 8:30 PM – 9:20 PM
SWH 10061, Burnaby
E106 We 8:30 PM – 9:20 PM
SWH 10075, 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 Chris Zatzick
Tu 12:30 PM – 2:20 PM
SSCC 9002, Burnaby
D101 Tu 2:30 PM – 3:20 PM
WMC 2260, Burnaby
D102 Tu 2:30 PM – 3:20 PM
WMC 2532, Burnaby
D103 Tu 3:30 PM – 4:20 PM
WMC 2521, Burnaby
D104 Tu 3:30 PM – 4:20 PM
WMC 2532, Burnaby
D105 Tu 4:30 PM – 5:20 PM
BLU 10901, Burnaby
D106 Tu 4:30 PM – 5:20 PM
WMC 3515, Burnaby
D107 Tu 4:30 PM – 5:20 PM
WMC 2503, Burnaby
D200 Sam Thiara
Mo 10:30 AM – 12:20 PM
SRYC 3090, Surrey
D201 Mo 12:30 PM – 1:20 PM
SRYC 3010, Surrey
D202 Mo 12:30 PM – 1:20 PM
SRYC 3290, Surrey
D203 Mo 1:30 PM – 2:20 PM
SRYC 3010, Surrey
D204 Mo 1:30 PM – 2:20 PM
SRYC 3290, Surrey
D300 Natalya Alonso
Tu 2:30 PM – 4:20 PM
AQ 3182, Burnaby
D301 Tu 4:30 PM – 5:20 PM
WMC 2521, Burnaby
D302 Tu 4:30 PM – 5:20 PM
AQ 5047, Burnaby
D303 Tu 5:30 PM – 6:20 PM
RCB 6101, Burnaby
D304 Tu 5:30 PM – 6:20 PM
SWH 10075, Burnaby
D305 Tu 6:30 PM – 7:20 PM
RCB 6101, Burnaby
D306 Tu 6:30 PM – 7:20 PM
RCB 5125, Burnaby
E100 Natalya Alonso
Tu 5:30 PM – 7:20 PM
EDB 7618, Burnaby
E101 Tu 7:30 PM – 8:20 PM
RCB 6101, Burnaby
E102 Tu 7:30 PM – 8:20 PM
RCB 5125, Burnaby
E103 Tu 7:30 PM – 8:20 PM
SWH 10075, Burnaby
E104 Tu 3:30 PM – 4:20 PM
WMC 3511, 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, e.g. Python. The students will be exposed to diverse fields within, and applications of computing science. Topics will include: pseudocode; data types and control structures; fundamental algorithms; recursion; reading and writing files; measuring performance of algorithms; debugging tools; basic terminal navigation using shell commands. Treatment is informal and programming is presented as a problem-solving tool. Prerequisite: BC Math 12 or equivalent is recommended. Students with credit for CMPT 102, 128, 130 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129, 130 or 135 first may not then take this course for further credit. Quantitative/Breadth-Science.

Section Instructor Day/Time Location
D100 Hazra Imran
Mo 8:30 AM – 9:20 AM
Th 8:30 AM – 10:20 AM
AQ 3182, Burnaby
RCB IMAGTH, Burnaby
D200 Hazra Imran
Mo 12:30 PM – 1:20 PM
Th 12:30 PM – 2:20 PM
WMC 3520, Burnaby
RCB IMAGTH, Burnaby
D300 Mohammad Tayebi
Mo 10:30 AM – 12:20 PM
We 10:30 AM – 11:20 AM
SSCC 9002, Burnaby
SSCB 9201, Burnaby
D400 Bobby Chan
Mo 8:30 AM – 9:20 AM
Th 8:30 AM – 10:20 AM
SRYE 1002, Surrey
SRYE 1002, Surrey
D401 Bobby Chan
Th 10:30 AM – 11:20 AM
SRYE 4013, Surrey
D402 Bobby Chan
Th 11:30 AM – 12:20 PM
SRYE 4013, Surrey
D403 Bobby Chan
Th 12:30 PM – 1:20 PM
SRYE 4013, Surrey
D404 Bobby Chan
Th 1:30 PM – 2:20 PM
SRYE 4013, Surrey
D405 Bobby Chan
Th 2:30 PM – 3:20 PM
SRYE 4013, Surrey
D406 Bobby Chan
Th 3:30 PM – 4:20 PM
SRYE 4013, Surrey
D407 Bobby Chan
Th 4:30 PM – 5:20 PM
SRYE 4013, Surrey
D408 Bobby Chan
Th 5:30 PM – 6:20 PM
SRYE 4013, Surrey
CMPT 125 - Introduction to Computing Science and Programming II (3)

A rigorous introduction to computing science and computer programming, suitable for students who already have some background in computing science and programming. Intended for students who will major in computing science or a related program. Topics include: memory management; fundamental algorithms; formally analyzing the running time of algorithms; abstract data types and elementary data structures; object-oriented programming and software design; specification and program correctness; reading and writing files; debugging tools; shell commands. Prerequisite: CMPT 120 or CMPT 130, with a minimum grade of C-. Students with credit for CMPT 126, 129, 135 or CMPT 200 or higher may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Victor Cheung
Mo 2:30 PM – 4:20 PM
We 2:30 PM – 3:20 PM
SSCB 9200, Burnaby
WMC 3520, Burnaby
D101 Victor Cheung
Th 8:30 AM – 9:20 AM
ASB 9838, Burnaby
D102 Victor Cheung
Th 8:30 AM – 9:20 AM
ASB 9838, Burnaby
D103 Victor Cheung
Th 9:30 AM – 10:20 AM
ASB 9838, Burnaby
D104 Victor Cheung
Th 9:30 AM – 10:20 AM
ASB 9838, Burnaby
D105 Victor Cheung
Th 10:30 AM – 11:20 AM
ASB 9838, Burnaby
D106 Victor Cheung
Th 10:30 AM – 11:20 AM
ASB 9838, Burnaby
D107 Victor Cheung
Th 11:30 AM – 12:20 PM
ASB 9838, Burnaby
D108 Victor Cheung
Th 11:30 AM – 12:20 PM
ASB 9838, Burnaby
D200 Milan Tofiloski
We 9:30 AM – 10:20 AM
Fr 8:30 AM – 10:20 AM
SSCB 9201, Burnaby
SSCB 9201, Burnaby
D201 Milan Tofiloski
Mo 2:30 PM – 3:20 PM
ASB 9838, Burnaby
D202 Milan Tofiloski
Mo 2:30 PM – 3:20 PM
ASB 9838, Burnaby
D203 Milan Tofiloski
Mo 3:30 PM – 4:20 PM
ASB 9838, Burnaby
D204 Milan Tofiloski
Mo 3:30 PM – 4:20 PM
ASB 9838, Burnaby
D205 Milan Tofiloski
We 2:30 PM – 3:20 PM
ASB 9838, Burnaby
D206 Milan Tofiloski
We 2:30 PM – 3:20 PM
ASB 9838, Burnaby
D207 Milan Tofiloski
We 3:30 PM – 4:20 PM
ASB 9838, Burnaby
D208 Milan Tofiloski
We 3:30 PM – 4:20 PM
ASB 9838, 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, 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 Igor Shinkar
We 11:30 AM – 12:20 PM
Fr 10:30 AM – 12:20 PM
SWH 10081, Burnaby
SWH 10081, Burnaby
D101 Igor Shinkar
Tu 2:30 PM – 3:20 PM
ASB 9838, Burnaby
D102 Igor Shinkar
Tu 2:30 PM – 3:20 PM
ASB 9838, Burnaby
D103 Igor Shinkar
Tu 3:30 PM – 4:20 PM
ASB 9838, Burnaby
D104 Igor Shinkar
Tu 3:30 PM – 4:20 PM
ASB 9838, Burnaby
D105 Igor Shinkar
Tu 4:30 PM – 5:20 PM
ASB 9838, Burnaby
D106 Igor Shinkar
Tu 4:30 PM – 5:20 PM
ASB 9838, Burnaby
D107 Igor Shinkar
Tu 5:30 PM – 6:20 PM
ASB 9838, Burnaby
D108 Igor Shinkar
Tu 5:30 PM – 6:20 PM
ASB 9838, Burnaby
D200 John Edgar
We 11:30 AM – 12:20 PM
Fr 10:30 AM – 12:20 PM
SRYE 1002, Surrey
SRYE 1002, Surrey
D201 John Edgar
Fr 8:30 AM – 9:20 AM
SRYE 4013, Surrey
D203 John Edgar
Fr 9:30 AM – 10:20 AM
SRYE 4013, Surrey
D204 John Edgar
Fr 9:30 AM – 10:20 AM
SRYE 4024, Surrey
D207 John Edgar
Mo 9:30 AM – 10:20 AM
SRYE 4013, Surrey
D208 John Edgar
Mo 9:30 AM – 10:20 AM
SRYE 4024, Surrey
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 Saba Alimadadi Jani
Tu 12:30 PM – 2:20 PM
Fr 12:30 PM – 1:20 PM
AQ 3154, Burnaby
AQ 3005, Burnaby
D200 Rob Cameron
We 1:30 PM – 2:20 PM
Fr 12:30 PM – 2:20 PM
SRYE 3016, Surrey
SRYE 3016, Surrey
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 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 Anne Lavergne
Tu 4:30 PM – 6:20 PM
Fr 4:30 PM – 5:20 PM
WMC 3520, Burnaby
AQ 3181, Burnaby
D101 Anne Lavergne
Mo 8:30 AM – 9:20 AM
ASB 9838, Burnaby
D102 Anne Lavergne
Mo 8:30 AM – 9:20 AM
ASB 9838, Burnaby
D103 Anne Lavergne
Mo 9:30 AM – 10:20 AM
ASB 9838, Burnaby
D104 Anne Lavergne
Mo 9:30 AM – 10:20 AM
ASB 9838, Burnaby
D105 Anne Lavergne
Mo 10:30 AM – 11:20 AM
ASB 9838, Burnaby
D106 Anne Lavergne
Mo 10:30 AM – 11:20 AM
ASB 9838, Burnaby
D107 Anne Lavergne
Mo 11:30 AM – 12:20 PM
ASB 9838, Burnaby
D108 Anne Lavergne
Mo 11:30 AM – 12:20 PM
ASB 9838, Burnaby
D200 Anne Lavergne
Mo, We, Fr 8:30 AM – 9:20 AM
AQ 3181, Burnaby
D201 Anne Lavergne
Tu 8:30 AM – 9:20 AM
ASB 9838, Burnaby
D202 Anne Lavergne
Tu 8:30 AM – 9:20 AM
ASB 9838, Burnaby
D203 Anne Lavergne
Tu 9:30 AM – 10:20 AM
ASB 9838, Burnaby
D204 Anne Lavergne
Tu 9:30 AM – 10:20 AM
ASB 9838, Burnaby
D205 Anne Lavergne
Tu 10:30 AM – 11:20 AM
ASB 9838, Burnaby
D206 Anne Lavergne
Tu 10:30 AM – 11:20 AM
ASB 9838, Burnaby
D207 Anne Lavergne
Tu 11:30 AM – 12:20 PM
ASB 9838, Burnaby
D208 Anne Lavergne
Tu 11:30 AM – 12:20 PM
ASB 9838, 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.

Section Instructor Day/Time Location
D100 Brad Bart
Mo, We, Fr 11:30 AM – 12:20 PM
SSCB 9200, Burnaby
D101 Brad Bart
Tu 8:30 AM – 9:20 AM
AQ 5035, Burnaby
D102 Brad Bart
Tu 8:30 AM – 9:20 AM
WMC 3511, Burnaby
D103 Brad Bart
Tu 9:30 AM – 10:20 AM
WMC 2268, Burnaby
D104 Brad Bart
Tu 9:30 AM – 10:20 AM
WMC 2260, Burnaby
D105 Brad Bart
Tu 10:30 AM – 11:20 AM
AQ 5004, Burnaby
D106 Brad Bart
Tu 10:30 AM – 11:20 AM
WMC 3533, Burnaby
D107 Brad Bart
Tu 12:30 PM – 1:20 PM
AQ 5047, Burnaby
D108 Brad Bart
Tu 12:30 PM – 1:20 PM
WMC 3250, Burnaby
D200 Brad Bart
Mo, We, Fr 1:30 PM – 2:20 PM
AQ 3182, Burnaby
D201 Brad Bart
Mo 2:30 PM – 3:20 PM
AQ 5006, Burnaby
D202 Brad Bart
Mo 2:30 PM – 3:20 PM
AQ 5007, Burnaby
D203 Brad Bart
Mo 3:30 PM – 4:20 PM
WMC 2521, Burnaby
D204 Brad Bart
Mo 3:30 PM – 4:20 PM
AQ 5047, Burnaby
D205 Brad Bart
Mo 4:30 PM – 5:20 PM
WMC 2523, Burnaby
D206 Brad Bart
Mo 4:30 PM – 5:20 PM
WMC 2521, Burnaby
D207 Brad Bart
Mo 5:30 PM – 6:20 PM
WMC 3251, Burnaby
D208 Brad Bart
Mo 5:30 PM – 6:20 PM
AQ 5035, Burnaby
D300 Harinder Khangura
We 2:30 PM – 3:20 PM
Fr 2:30 PM – 4:20 PM
SRYC 2600, Surrey
SRYC 2600, Surrey
D301 Harinder Khangura
Mo 12:30 PM – 1:20 PM
SRYC 2990, Surrey
D302 Harinder Khangura
Mo 12:30 PM – 1:20 PM
SRYC 2995, Surrey
D303 Harinder Khangura
Mo 1:30 PM – 2:20 PM
SRYC 2990, Surrey
D304 Harinder Khangura
Mo 1:30 PM – 2:20 PM
SRYC 2995, Surrey
D305 Harinder Khangura
Mo 2:30 PM – 3:20 PM
SRYC 2990, Surrey
D306 Harinder Khangura
Mo 2:30 PM – 3:20 PM
SRYC 2995, Surrey
D307 Harinder Khangura
Mo 3:30 PM – 4:20 PM
SRYC 2990, Surrey
D308 Harinder Khangura
Mo 3:30 PM – 4:20 PM
SRYC 2995, Surrey
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.

Section Instructor Day/Time Location
D100 Nathan Ilten
Hanmeng Zhan
Mo, We, Fr 12:30 PM – 1:20 PM
SSCC 9002, Burnaby
D300 Nils Bruin
Mo, We, Fr 12:30 PM – 1:20 PM
EDB 7618, Burnaby
D400 Abraham Punnen
Mo, We, Fr 8:30 AM – 9:20 AM
SRYC 5280, Surrey
OP01 TBD
OP02 TBD
OP03 TBD

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 Joanna Niezen
Mo, We, Fr 8:30 AM – 9:20 AM
SSCB 9200, Burnaby
D101 Tu 8:30 AM – 9:20 AM
SWH 10061, Burnaby
D102 Tu 9:30 AM – 10:20 AM
SWH 10061, Burnaby
D105 We 12:30 PM – 1:20 PM
SWH 10061, Burnaby
D106 We 1:30 PM – 2:20 PM
SWH 10061, Burnaby
D107 We 4:30 PM – 5:20 PM
SWH 10061, Burnaby
D108 We 3:30 PM – 4:20 PM
SWH 10061, Burnaby
D400 Natalia Kouzniak
Mo, We, Fr 11:30 AM – 12:20 PM
SRYC 3170, Surrey
D401 Natalia Kouzniak
We 1:30 PM – 2:20 PM
SRYC 2740, Surrey
D402 Natalia Kouzniak
We 2:30 PM – 3:20 PM
SRYC 2740, Surrey
D403 Natalia Kouzniak
Fr 2:30 PM – 3:20 PM
SRYC 2740, Surrey
OP01 TBD
OP02 TBD
MATH 151 - Calculus I (3)

Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Logarithmic and exponential functions, trigonometric functions, inverse functions. Limits, continuity, and derivatives. Techniques of differentiation, including logarithmic and implicit differentiation. The Mean Value Theorem. Applications of differentiation including extrema, curve sketching, Newton's method. Introduction to modeling with differential equations. Polar coordinates, parametric curves. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least A, or MATH 100 with a grade of at least B, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 154 or 157 may not take MATH 151 for further credit. Quantitative.

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

Designed for students specializing in the life sciences. Topics include: limits, growth rate and the derivative; elementary functions, optimization and approximation methods, and their applications, integration, and differential equations; mathematical models of biological processes and their implementation and analysis using software. Prerequisite: Pre-Calculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C-, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 151 or 157 may not take MATH 154 for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Elisha Are
Mo, We, Fr 8:30 AM – 9:20 AM
SWH 10081, Burnaby
OP01 TBD
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 Marni Julie Mishna
Mo, We, Fr 11:30 AM – 12:20 PM
RCB IMAGTH, Burnaby
D400 Justin Chan
Mo, We, Fr 12:30 PM – 1:20 PM
SRYC 5280, Surrey
OP01 TBD
OP02 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 Ralf Wittenberg
Mo, We, Fr 8:30 AM – 9:20 AM
SSCC 9001, Burnaby
D200 Jamie Mulholland
Mo, We, Fr 8:30 AM – 9:20 AM
BLU 10011, Burnaby
D300 Nathan Ilten
Mo, We, Fr 8:30 AM – 9:20 AM
WMC 3520, Burnaby
D400 Luis Goddyn
Mo, We, Fr 11:30 AM – 12:20 PM
SRYC 2600, Surrey
OP01 TBD
OP02 TBD
OP03 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.

Section Instructor Day/Time Location
D100 Cedric Chauve
Mo 2:30 PM – 4:20 PM
We 2:30 PM – 3:20 PM
WMC 3220, Burnaby
WMC 3220, Burnaby
D101 Tu 3:30 PM – 4:20 PM
AQ 5016, Burnaby

and one of

MATH 232 - Applied Linear Algebra (3)

Linear equations, matrices, determinants. Introduction to vector spaces and linear transformations and bases. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. An emphasis on applications involving matrix and vector calculations. Prerequisite: MATH 150 or 151 or MACM 101, with a minimum grade of C-; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 240 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Brenda Davison
Mo, We, Fr 11:30 AM – 12:20 PM
SSCC 9001, Burnaby
D400 Abraham Punnen
Mo, We, Fr 1:30 PM – 2:20 PM
SRYC 5280, Surrey
OP01 TBD
OP02 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 Katrina Honigs
Mo, We, Fr 11:30 AM – 12:20 PM
SSCC 9000, Burnaby
OP01 TBD

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, BUS 232, or ECON 233, and one of CMPT 102, CMPT 120, CMPT 125, CMPT 128, CMPT 129, CMPT 130, all with a minimum grade of C- or permission of the instructor. STAT 260 is also recommended. Quantitative.

Section Instructor Day/Time Location
D100 Lloyd Elliott
Mo 12:30 PM – 2:20 PM
WMC 3260, Burnaby
D101 Lloyd Elliott
Mo 2:30 PM – 3:20 PM
AQ 3148.2, Burnaby
D102 Lloyd Elliott
Mo 3:30 PM – 4:20 PM
AQ 3148.2, Burnaby
D103 Lloyd Elliott
Mo 4:30 PM – 5:20 PM
AQ 3148.2, Burnaby
D104 Lloyd Elliott
Mo 5:30 PM – 6:20 PM
AQ 3148.2, Burnaby
D106 Lloyd Elliott
Tu 6:30 PM – 7:20 PM
AQ 3148.1, Burnaby
D107 Lloyd Elliott
Tu 7:30 PM – 8:20 PM
AQ 3148.2, Burnaby
STAT 260 - Introductory R for Data Science (2)

An introduction to the R programming language for data science. Exploring data: visualization, transformation and summaries. Data wrangling: reading, tidying, and data types. No prior computer programming experience required. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a grade of at least C- or permission of the instructor. Corequisite: STAT 261. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.

Section Instructor Day/Time Location
D100 Haolun Shi
Tu 12:30 PM – 2:20 PM
WMC 3260, Burnaby
STAT 261 - Laboratory for Introductory R for Data Science (1)

A hands-on application of the R programming language for data science. Using the R concepts covered in STAT 260, students will explore (visualize, transform, and summarize) and wrangle (read and tidy) data. No prior computer programming experience required. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, ECON 233, or POL 201, with a grade of at least C- or permission of the instructor. Corequisite: STAT 260. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.

Section Instructor Day/Time Location
D100 Haolun Shi
Tu 2:30 PM – 3:20 PM
AQ 3148.2, Burnaby
D200 Haolun Shi
Tu 3:30 PM – 4:20 PM
AQ 3148.2, Burnaby
D300 Haolun Shi
Tu 4:30 PM – 5:20 PM
AQ 3148.2, Burnaby
D400 Haolun Shi
Tu 5:30 PM – 6:20 PM
AQ 3148.2, Burnaby
D600 Haolun Shi
We 4:30 PM – 5:20 PM
AQ 3148.2, Burnaby
D700 Haolun Shi
We 5:30 PM – 6:20 PM
AQ 3148.2, Burnaby

and one of

BUS 232 - Business Statistics (3)

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

Section Instructor Day/Time Location
D100 Mohammad Ghotboddini
Mo 2:30 PM – 5:20 PM
AQ 3182, Burnaby
D200 Hamideh Shokoohian
Th 2:30 PM – 5:20 PM
SRYC 5240, Surrey
E100 Negar Ganjouhaghighi
We 4:30 PM – 7:20 PM
WMC 3520, Burnaby
OP01 Mo 9:30 AM – 1:20 PM
WMC 2303, Burnaby
OP02 We 9:30 AM – 1:20 PM
WMC 2303, Burnaby
OP03 Fr 9:30 AM – 11:20 AM
WMC 2303, Burnaby
OP04 Th 9:30 AM – 11:20 AM
SRYC 3300, Surrey
OP05 Th 12:30 PM – 2:20 PM
SRYC 3300, Surrey
OP06 Mo 5:30 PM – 8:20 PM
WMC 2303, Burnaby
OP07 We 1:30 PM – 3:20 PM
WMC 2303, Burnaby
OP09 Mo 4:30 PM – 5:20 PM
WMC 2301, Burnaby
OP10 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
D900 Wei Lin
Mo 12:30 PM – 1:20 PM
Th 12:30 PM – 2:20 PM
SRYE 1002, Surrey
SRYE 1002, Surrey
OL01 Distance Education
OP09 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 200W, 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
D100 Marie Loughin
Mo 10:30 AM – 12:20 PM
We 10:30 AM – 11:20 AM
SSCB 9201, Burnaby
AQ 3181, Burnaby
OL01 Distance Education
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
Mo, We, Fr 9:30 AM – 10:20 AM
SSCB 9200, Burnaby
D900 Scott Pai
Tu 10:30 AM – 12:20 PM
Th 10:30 AM – 11:20 AM
SRYC 2750, Surrey
SRYC 2750, Surrey
OL01 Distance Education
OP01 TBD
OP09 TBD

Upper Division Requirements

Students complete a minimum of 43-44 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 Miremad Soleymanian
Tu 10:30 AM – 12:20 PM
WMC 3520, Burnaby
D101 Tu 12:30 PM – 1:20 PM
RCB 8105, Burnaby
D102 Tu 12:30 PM – 1:20 PM
RCB 8104, Burnaby
D103 Tu 12:30 PM – 1:20 PM
RCB 8106, Burnaby
D104 Tu 1:30 PM – 2:20 PM
AQ 5048, Burnaby
D105 Tu 1:30 PM – 2:20 PM
AQ 2120, Burnaby
D106 Tu 1:30 PM – 2:20 PM
RCB 8105, Burnaby
D107 Tu 2:30 PM – 3:20 PM
WMC 2523, Burnaby
D108 Tu 2:30 PM – 3:20 PM
WMC 2521, Burnaby
D109 Tu 2:30 PM – 3:20 PM
AQ 5047, Burnaby
D110 Tu 3:30 PM – 4:20 PM
AQ 5017, Burnaby
D111 Tu 3:30 PM – 4:20 PM
AQ 5028, Burnaby
D200 Miremad Soleymanian
Th 2:30 PM – 4:20 PM
SRYC 5140, Surrey
D201 Th 4:30 PM – 5:20 PM
SRYC 3270, Surrey
D202 Th 4:30 PM – 5:20 PM
SRYC 5100, Surrey
D203 Th 5:30 PM – 6:20 PM
SRYC 3270, Surrey
D204 Th 5:30 PM – 6:20 PM
SRYC 3120, Surrey
D205 Th 5:30 PM – 6:20 PM
SRYC 5100, Surrey
BUS 360W - Business Communication (4)

Helps students develop professional writing- and speaking-based communication strategies they can confidently adapt to a wide range of business situations. The course aims to raise their communication performance to a professionally acceptable level, rather than to memorize or theorize about communication knowledge: this is a “learn-by-doing” course. Students will improve their ability to conceptualize, analyze/evaluate, synthesize, and apply information to guide their thinking and finished products across various business contexts. As teamwork is a fundamental skill valued by employers, students will participate in a major team project to learn about and apply best practices for collaboration with respect to professional business communication. The primary means of instruction and learning is guided practice in both writing and presenting in response to realistic business contexts. Instruction and assessment focus on both the process of creating professional, finished products, as well as the quality of those products. 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 (BUS 201 or BUS 202) or BUS 301, with a minimum grade of C-, OR to students admitted Fall 2017 – Summer 2022 to the business administration major, honours, or second degree program and who have 45 units and BUS 130 or (BUS 201 or BUS 202) or BUS 301, and BUS 217W, both with a minimum grade of C-, OR to students admitted Fall 2022 onwards to the business administration major, honours, or second degree program, and who have 45 units; BUS 217W and (BUS 201 or BUS 202), both with a minimum grade of C-; and Corequisite: BUS 300, 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
D300 Kevin Stewart
Th 11:30 AM – 2:20 PM
BLU 10031, Burnaby
D400 Leanne Barlow
Tu 11:30 AM – 2:20 PM
BLU 10031, Burnaby
D500 Christian Venhuizen
Th 11:30 AM – 2:20 PM
SRYC 5080, Surrey
D600 Christian Venhuizen
We 2:30 PM – 5:20 PM
SRYC 5080, Surrey
D700 Leanne Barlow
Th 8:30 AM – 11:20 AM
WMC 2210, Burnaby
E100 Eric Tung
Mo 5:30 PM – 8:20 PM
WMC 3253, Burnaby
E200 Eric Tung
Th 5:30 PM – 8:20 PM
WMC 3253, Burnaby
E300 Kevin Stewart
Tu 6:30 PM – 9:20 PM
WMC 3253, Burnaby
E400 Kevin Stewart
We 6:30 PM – 9:20 PM
WMC 3253, 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.

Section Instructor Day/Time Location
D100 Jason Ho
Tu 11:30 AM – 2:20 PM
WMC 2305, Burnaby
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
Th 11:30 AM – 2:20 PM
WMC 2305, 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 254), all with a minimum grade of C-.

Section Instructor Day/Time Location
D100 Steven Ko
Th 11:30 AM – 2:20 PM
SWH 10081, Burnaby
D200 Harinder Khangura
Mo 10:30 AM – 12:20 PM
We 10:30 AM – 11:20 AM
SRYE 2016, Surrey
SRYE 2016, Surrey
CMPT 307 - Data Structures and Algorithms (3)

Design and analysis of efficient data structures and algorithms. General techniques for building and analyzing algorithms (greedy, divide & conquer, dynamic programming, network flows). Introduction to NP-completeness. Prerequisite: CMPT 225, (MACM 201 or CMPT 210), (MATH 150 or MATH 151), and (MATH 232 or MATH 240), all with a minimum grade of C-. MATH 154 or MATH 157 with a grade of at least B+ may be substituted for MATH 150 or MATH 151.

Section Instructor Day/Time Location
D100 Qianping Gu
Mo 1:30 PM – 2:20 PM
We 1:30 PM – 2:20 PM
Fr 1:30 PM – 2:20 PM
SSCC 9002, Burnaby
WMC 3260, Burnaby
WMC 3260, Burnaby
E100 Mohammadmahdi Jahanara
Tu 4:30 PM – 6:20 PM
Th 4:30 PM – 5:20 PM
AQ 3005, Burnaby
SSCB 9201, 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, STAT 271, ENSC 280, or MSE 210), with a minimum grade of C-.

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 Martin Ester
Mo 10:30 AM – 11:20 AM
Th 10:30 AM – 12:20 PM
WMC 3260, Burnaby
WMC 3260, Burnaby
D300 John Edgar
Mo, We, Fr 8:30 AM – 9:20 AM
SRYE 2016, Surrey
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 Ke Wang
Mo 12:30 PM – 2:20 PM
We 12:30 PM – 1:20 PM
AQ 3005, Burnaby
AQ 3005, 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.

Section Instructor Day/Time Location
D100 Ladislav Stacho
Mo, We, Fr 2:30 PM – 3:20 PM
WMC 3260, Burnaby
D101 Tu 2:30 PM – 3:20 PM
WMC 2830, Burnaby
D102 Tu 3:30 PM – 4:20 PM
WMC 2830, Burnaby
D103 Tu 4:30 PM – 5:20 PM
WMC 2830, Burnaby
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 with a minimum grade of C- or ECON 113 with a minimum grade of A-; ECON 105 with a minimum grade of C- or ECON 115 with a minimum grade of A-; 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 Marie Rekkas
Mo 8:30 AM – 9:20 AM
Th 8:30 AM – 10:20 AM
BLU 9660, Burnaby
SWH 10041, Burnaby
D101 Mo 9:30 AM – 10:20 AM
BLU 11911, Burnaby
D103 Mo 10:30 AM – 11:20 AM
AQ 5047, Burnaby
D104 Mo 11:30 AM – 12:20 PM
AQ 5047, Burnaby
D105 Mo 2:30 PM – 3:20 PM
AQ 5014, Burnaby
D110 We 12:30 PM – 1:20 PM
AQ 5047, 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 following programs: statistics major, statistics honours, actuarial science major, and actuarial science honours. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, with a minimum grade of C-. Students who have taken STAT 350 first may not then take the course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Owen Ward
Mo 12:30 PM – 2:20 PM
We 12:30 PM – 1:20 PM
SSCB 9200, Burnaby
SSCB 9200, Burnaby
OL01 Distance Education
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 following programs: statistics major, statistics honours, actuarial science major, and actuarial science honours. Prerequisite: One of STAT 201, STAT 203, STAT 205, STAT 270, BUS 232, or ECON 233, with a minimum grade of C-. Students who have taken STAT 350 first may not then take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Thomas Loughin
Mo 10:30 AM – 11:20 AM
Th 10:30 AM – 12:20 PM
AQ 3181, Burnaby
AQ 3181, Burnaby
OP01 TBD
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 ECON 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.

Section Instructor Day/Time Location
E100 Derek Bingham
Mo 4:30 PM – 6:20 PM
We 4:30 PM – 5:20 PM
AQ 3154, Burnaby
AQ 3005, Burnaby
E101 Derek Bingham
Tu 5:30 PM – 6:20 PM
RCB 5120, Burnaby
E102 Derek Bingham
Tu 6:30 PM – 7:20 PM
AQ 2104, Burnaby
STAT 452 - Statistical Learning and Prediction (3)

An introduction to the essential modern supervised and unsupervised statistical learning methods. Topics include review of linear regression, classification, statistical error measurement, flexible regression and classification methods, clustering and dimension reduction. Prerequisite: STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

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

Section Instructor Day/Time Location
E100 Donald Estep
Tu 5:30 PM – 6:20 PM
Th 4:30 PM – 6:20 PM
AQ 3182, Burnaby
AQ 3182, Burnaby
E101 Donald Estep
Tu 6:30 PM – 7:20 PM
AQ 5006, Burnaby
E102 Donald Estep
Tu 7:30 PM – 8:20 PM
AQ 5006, Burnaby
E103 Donald Estep
Mo 4:30 PM – 5:20 PM
AQ 5006, Burnaby
E104 Donald Estep
Mo 5:30 PM – 6:20 PM
AQ 5006, Burnaby
E105 Donald Estep
Mo 6:30 PM – 7:20 PM
AQ 5006, Burnaby
STAT 475 - Applied Discrete Data Analysis (3)

Introduction to standard methodology for analyzing categorical data including chi-squared tests for two- and multi-way contingency tables, logistic regression, and loglinear (Poisson) regression. Prerequisite: STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-. Students with credit for the former STAT 402 or 602 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Thomas Loughin
Tu 2:30 PM – 4:20 PM
Fr 3:30 PM – 4:20 PM
AQ 3154, Burnaby
AQ 3154, Burnaby
D101 Thomas Loughin
We 12:30 PM – 1:20 PM
AQ 5007, Burnaby
D102 Thomas Loughin
We 1:30 PM – 2:20 PM
AQ 5007, Burnaby
D103 Thomas Loughin
We 3:30 PM – 4:20 PM
BLU 10655, Burnaby
D104 Thomas Loughin
We 4:30 PM – 5:20 PM
AQ 5007, Burnaby
STAT 485 - Applied Time Series Analysis (3)

Introduction to linear time series analysis including moving average, autoregressive and ARIMA models, estimation, data analysis, forecasting errors and confidence intervals, conditional and unconditional models, and seasonal models. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. This course may not be taken for further credit by students who have credit for ECON 484. Quantitative.

Upper Division Recommended Courses

BUS 345 - Marketing Research (3)

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
Mo 9:30 AM – 12:20 PM
WMC 2503, 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
D200 Eman Nasr
Fr 8:30 AM – 10:20 AM
SRYC 3310, Surrey
D201 Fr 10:30 AM – 12:20 PM
SRYC 3300, Surrey
D202 Fr 12:30 PM – 2:20 PM
SRYC 3130, Surrey
D203 Fr 2:30 PM – 4:20 PM
SRYC 3130, Surrey
E100 Eman Nasr
Tu 4:30 PM – 6:20 PM
WMC 2202, Burnaby
E101 Tu 6:30 PM – 8:20 PM
WMC 2301, Burnaby
E102 Tu 6:30 PM – 8:20 PM
WMC 2305, 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.

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.

Section Instructor Day/Time Location
E100 Mohammad Ghotboddini
Mo 6:00 PM – 9:50 PM
WMC 2301, Burnaby
CMPT 308 - Computability and Complexity (3)

Formal models of computation such as automata and Turing machines. Decidability and undecidability. Recursion Theorem. Connections between computability and logic (Gödel’s Incompleteness). Time and space complexity classes. NP-completeness. Prerequisite: (MACM 201 or CMPT 210) with a minimum grade of C-.

CMPT 310 - Introduction to Artificial Intelligence (3)

A survey of modern approaches for artificial intelligence (AI). Provides an introduction to a variety of AI topics and prepares students for upper-level courses. Topics include: problem solving with search; adversarial game playing; probability and Bayesian networks; machine learning; and applications such as robotics, visual computing and natural language. 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 Hang Ma
Tu 11:30 AM – 2:20 PM
EDB 7618, Burnaby
E100 Milan Tofiloski
Tu 4:30 PM – 7:20 PM
SSCB 9200, 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
D100 Frederick Popowich
Tu 2:30 PM – 4:20 PM
Fr 2:30 PM – 3:20 PM
AQ 3150, Burnaby
AQ 3154, Burnaby
D200 Frederick Popowich
Mo 2:30 PM – 4:20 PM
We 2:30 PM – 3:20 PM
AQ 3003, Burnaby
AQ 3153, 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-.

Section Instructor Day/Time Location
D100 Qianping Gu
Mo, We, Fr 9:30 AM – 10:20 AM
AQ 3150, Burnaby
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.

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 Steven Ruuth
Mo, We, Fr 12:30 PM – 1:20 PM
AQ 3181, Burnaby
D101 We 2:30 PM – 3:20 PM
WMC 2830, Burnaby
D102 We 3:30 PM – 4:20 PM
WMC 2830, Burnaby
D103 We 4:30 PM – 5:20 PM
WMC 2830, Burnaby
D104 Th 9:30 AM – 10:20 AM
WMC 2830, Burnaby
D105 Th 10:30 AM – 11:20 AM
WMC 2830, Burnaby
D106 Th 11:30 AM – 12:20 PM
WMC 2830, 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 ECON 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 ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

Section Instructor Day/Time Location
E100 Donald Estep
Tu 5:30 PM – 6:20 PM
Th 4:30 PM – 6:20 PM
AQ 3182, Burnaby
AQ 3182, Burnaby
E101 Donald Estep
Tu 6:30 PM – 7:20 PM
AQ 5006, Burnaby
E102 Donald Estep
Tu 7:30 PM – 8:20 PM
AQ 5006, Burnaby
E103 Donald Estep
Mo 4:30 PM – 5:20 PM
AQ 5006, Burnaby
E104 Donald Estep
Mo 5:30 PM – 6:20 PM
AQ 5006, Burnaby
E105 Donald Estep
Mo 6:30 PM – 7:20 PM
AQ 5006, Burnaby
STAT 475 - Applied Discrete Data Analysis (3)

Introduction to standard methodology for analyzing categorical data including chi-squared tests for two- and multi-way contingency tables, logistic regression, and loglinear (Poisson) regression. Prerequisite: STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-. Students with credit for the former STAT 402 or 602 may not take this course for further credit. Quantitative.

Section Instructor Day/Time Location
D100 Thomas Loughin
Tu 2:30 PM – 4:20 PM
Fr 3:30 PM – 4:20 PM
AQ 3154, Burnaby
AQ 3154, Burnaby
D101 Thomas Loughin
We 12:30 PM – 1:20 PM
AQ 5007, Burnaby
D102 Thomas Loughin
We 1:30 PM – 2:20 PM
AQ 5007, Burnaby
D103 Thomas Loughin
We 3:30 PM – 4:20 PM
BLU 10655, Burnaby
D104 Thomas Loughin
We 4:30 PM – 5:20 PM
AQ 5007, Burnaby
STAT 485 - Applied Time Series Analysis (3)

Introduction to linear time series analysis including moving average, autoregressive and ARIMA models, estimation, data analysis, forecasting errors and confidence intervals, conditional and unconditional models, and seasonal models. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. This course may not be taken for further credit by students who have credit for ECON 484. Quantitative.

University Degree Requirements

Students must also satisfy University degree requirements for degree completion.

Writing, Quantitative, and Breadth Requirements

Students admitted to Simon Fraser University beginning in the fall 2006 term must meet writing, quantitative and breadth requirements as part of any degree program they may undertake. See Writing, Quantitative, and Breadth Requirements for 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, and Statistics. Units used to satisfy DATA upper division requirements beyond these 34 can be applied simultaneously to the other major, minor or honours.