Please note:
To view the current Academic Calendar, go to www.sfu.ca/students/calendar.html.
Operations Research Honours
This program prepares students for careers in industry or a variety of graduate and professional programs.
Prerequisite Grade Requirement
To enroll in a course offered by the Department of Mathematics, a student must obtain a grade of C or better in each prerequisite course. Some courses may require higher prerequisite grades. Check the MATH course’s Calendar description for details.
Students will not normally be permitted to enroll in any course for which a D grade or lower was obtained in any prerequisite. No student may complete, for further credit, any course offered by the Department of Mathematics which is a prerequisite for a course the student has already completed with a grade of C or higher, without permission of the department.
Program Requirements
The program requires the completion of 120 units. The Faculty of Science stipulates that a minimum of 48 units must be in upper division, and that additional upper division units will be required to total a minimum of 60.
The specific requirements for this particular program are divided into three parts: required lower division courses, required upper division courses, and completion of an interdisciplinary requirement.
In addition to the program requirements set out below, general university regulations must be met.
A minimum program 3.00 cumulative grade point average (CGPA) must be obtained on the overall major program requirements, as well as a minimum program 3.00 grade point average in the upper division major courses.
Lower Division Requirements
Students complete
both of
An elementary introduction to computing science and computer programming, suitable for students with little or no programming background. Students will learn fundamental concepts and terminology of computing science, acquire elementary skills for programming in a highlevel language and be exposed to diverse fields within, and applications of computing science. Topics will include: pseudocode, data types and control structures, fundamental algorithms, computability and complexity, computer architecture, and history of computing science. Treatment is informal and programming is presented as a problemsolving tool. Prerequisite: BC Math 12 or equivalent is recommended. Students with credit for CMPT 102, 128, 130 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129, 130 or 135 first may not then take this course for further credit. Quantitative/BreadthScience.
Section  Instructor  Day/Time  Location 

D100 
Angelica Lim 
Mo, We, Fr 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby 
D200 
Angelica Lim 
Mo, We, Fr 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 
D400 
Harinder Khangura 
Mo, We, Fr 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby 
D401 
Th 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 

D402 
Th 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 

D403 
Th 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby 

D404 
Th 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 

D405 
Th 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 

D406 
Th 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 

D407 
Th 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D408 
Th 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 
A second course in computing science and programming intended for students studying mathematics, statistics or actuarial science and suitable for students who already have some background in computing science and programming. Topics include: a review of the basic elements of programming: use and implementation of elementary data structures and algorithms; fundamental algorithms and problem solving; basic objectoriented programming and software design; computation and computability and specification and program correctness. Prerequisite: CMPT 102 or CMPT 120. Students with credit for CMPT 125 or 135 may not take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Janice Regan 
Mo, We, Fr 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 
D101 
We 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby 

D102 
We 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby 

D103 
We 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 

D104 
We 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 

D105 
We 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 

D106 
We 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 

D107 
We 5:30 PM – 6:20 PM 
REMOTE LEARNING, Burnaby 

D108 
We 5:30 PM – 6:20 PM 
REMOTE LEARNING, Burnaby 
(Students transferring into a math program should contact the math undergraduate advisor if they have already completed equivalent courses.)
or both of
An introduction to computing science and computer programming, using a systems oriented language, such as C or C++. This course introduces basic computing science concepts. Topics will include: elementary data types, control structures, functions, arrays and strings, fundamental algorithms, computer organization and memory management. Prerequisite: BC Math 12 (or equivalent, or any of MATH 100, 150, 151, 154, or 157). Students with credit for CMPT 102, 120, 128 or 166 may not take this course for further credit. Students who have taken CMPT 125, 129 or 135 first may not then take this course for further credit. Quantitative/BreadthScience.
A second course in systemsoriented programming and computing science that builds upon the foundation set in CMPT 130 using a systemsoriented language such as C or C++. Topics: a review of the basic elements of programming; introduction to objectoriented programming (OOP); techniques for designing and testing programs; use and implementation of elementary data structures and algorithms; introduction to embedded systems programming. Prerequisite: CMPT 130. Students with credit for CMPT 125, 126, or 129 may not take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Toby Donaldson 
Mo, We, Fr 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 
D101 
Th 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 

D102 
Th 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 

D103 
Th 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D104 
Th 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D105 
Th 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D106 
Th 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D107 
Th 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 

D108 
Th 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 
and all of
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; objectoriented 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). Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Igor Shinkar 
Mo, We, Fr 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 
D101 
We 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 

D102 
We 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 

D103 
We 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 

D104 
We 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 

D105 
We 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D106 
We 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D107 
We 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D108 
We 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D200 
Mo, We, Fr 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 

D201 
We 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 

D202 
We 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 

D203 
We 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 

D204 
We 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 

D205 
We 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D206 
We 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D207 
We 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D208 
We 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 
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/BreadthScience.
Section  Instructor  Day/Time  Location 

D100 
Andrei Bulatov 
Mo, We, Fr 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 
D101 
Th 2:30 PM – 3:20 PM 
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 

D104 
Th 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D105 
Th 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 

D106 
Th 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 

D107 
Th 5:30 PM – 6:20 PM 
REMOTE LEARNING, Burnaby 

D108 
Th 5:30 PM – 6:20 PM 
REMOTE LEARNING, Burnaby 

D200 
Harinder Khangura 
Mo, We, Fr 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 
D201 
Th 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 

D202 
Th 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 

D203 
Th 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 

D204 
Th 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 

D205 
Th 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D206 
Th 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D207 
Th 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D208 
Th 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 
A continuation of MACM 101. Topics covered include graph theory, trees, inclusionexclusion, 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 
Michael Monagan 
Mo, We, Fr 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 
D200 
Mahsa Faizrahnemoon 
Mo, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
D300 
Mo, We, Fr 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 

OP01  TBD  
OP02  TBD  
OP03  TBD 
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. Students with credit for MATH 208 may not take this course for further credit. Writing/Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Tamon Stephen 
We 11:30 AM – 12:20 PM Fr 10:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby REMOTE LEARNING, Burnaby 
D101 
Tu 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 
Rectangular, cylindrical and spherical coordinates. Vectors, lines, planes, cylinders, quadric surfaces. Vector functions, curves, motion in space. Differential and integral calculus of several variables. Vector fields, line integrals, fundamental theorem for line integrals, Green's theorem. Prerequisite: MATH 152; or MATH 155 or MATH 158 with a grade of at least B. Recommended: It is recommended that MATH 240 or 232 be taken before or concurrently with MATH 251. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Jamie Mulholland 
Mo, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
OP01  TBD 
Basic laws of probability, sample distributions. Introduction to statistical inference and applications. Prerequisite: or Corequisite: MATH 152 or 155 or 158. 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 
Derek Bingham 
Mo, We, Fr 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 
OP01  TBD 
This course is a continuation of STAT 270. Review of probability models. Procedures for statistical inference using survey results and experimental data. Statistical model building. Elementary design of experiments. Regression methods. Introduction to categorical data analysis. Prerequisite: STAT 270 and one of MATH 152, MATH 155, or MATH 158. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Liangliang Wang 
Tu 10:30 AM – 12:20 PM Fr 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby REMOTE LEARNING, Burnaby 
D101 
Mo 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 

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

D103 
Mo 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D104 
Mo 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 
and one of
Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Topics as for Math 151 with a more extensive review of functions, their properties and their graphs. Recommended for students with no previous knowledge of Calculus. In addition to regularly scheduled lectures, students enrolled in this course are encouraged to come for assistance to the Calculus Workshop (Burnaby), or Math Open Lab (Surrey). Prerequisite: PreCalculus 12 (or equivalent) with a grade of at least B+, or MATH 100 with a grade of at least B, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 151, 154 or 157 may not take MATH 150 for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Sophie Burrill 
Mo, Tu, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
D200 
Natalia Kouzniak 
Mo, We, Fr 11:30 AM – 12:20 PM We 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby REMOTE LEARNING, Burnaby 
OP01  TBD  
OP02  TBD  
OP03  TBD 
Designed for students specializing in mathematics, physics, chemistry, computing science and engineering. Logarithmic and exponential functions, trigonometric functions, inverse functions. Limits, continuity, and derivatives. Techniques of differentiation, including logarithmic and implicit differentiation. The Mean Value Theorem. Applications of differentiation including extrema, curve sketching, Newton's method. Introduction to modeling with differential equations. Polar coordinates, parametric curves. Prerequisite: PreCalculus 12 (or equivalent) with a grade of at least A, or MATH 100 with a grade of at least B, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 154 or 157 may not take MATH 151 for further credit. Quantitative.
Designed for students specializing in the biological and medical sciences. Topics include: limits, growth rate and the derivative; elementary functions, optimization and approximation methods, and their applications; mathematical models of biological processes. Prerequisite: PreCalculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 151 or 157 may not take MATH 154 for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Luis Goddyn 
Mo, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
OP01  TBD  
OP02  TBD 
Designed for students specializing in business or the social sciences. Topics include: limits, growth rate and the derivative; logarithmic, exponential and trigonometric functions and their application to business, economics, optimization and approximation methods; introduction to functions of several variables with emphasis on partial derivatives and extrema. Prerequisite: PreCalculus 12 (or equivalent) with a grade of at least B, or MATH 100 with a grade of at least C, or achieving a satisfactory grade on the Simon Fraser University Calculus Readiness Test. Students with credit for either MATH 150, 151 or 154 may not take MATH 157 for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Randall Pyke Justin Chan 
Mo, We, Fr 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 
D200 
Mo, We, Fr 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 

OP01  TBD  
OP02  TBD 
and one of
Riemann sum, Fundamental Theorem of Calculus, definite, indefinite and improper integrals, approximate integration, integration techniques, applications of integration. Firstorder separable differential equations and growth models. Sequences and series, series tests, power series, convergence and applications of power series. Prerequisite: MATH 150 or 151; or MATH 154 or 157 with a grade of at least B. Students with credit for MATH 155 or 158 may not take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Vijaykumar Singh 
Mo, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
D200 
Brenda Davison 
Mo, We, Fr 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 
D300 
Brenda Davison 
Mo, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
OP01  TBD  
OP02  TBD  
OP03  TBD 
Designed for students specializing in the biological and medical sciences. Topics include: the integral, partial derivatives, differential equations, linear systems, and their applications; mathematical models of biological processes. Prerequisite: MATH 150, 151 or 154; or MATH 157 with a grade of at least B. Students with credit for MATH 152 or 158 may not take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Jonathan Jedwab Natalia Kouzniak 
Mo, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
D200 
Natalia Kouzniak Jonathan Jedwab 
Mo, We, Fr 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 
OP01  TBD  
OP02  TBD 
Designed for students specializing in business or the social sciences. Topics include: theory of integration, integration techniques, applications of integration; functions of several variables with emphasis on double and triple integrals and their applications; introduction to differential equations with emphasis on some special firstorder equations and their applications; sequences and series. Prerequisite: MATH 150 or 151 or 154 or 157. Students with credit for MATH 152 or 155 may not take MATH 158 for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

E100 
Mo 4:30 PM – 5:20 PM We 4:30 PM – 6:20 PM 
REMOTE LEARNING, Burnaby REMOTE LEARNING, Burnaby 

OP01  TBD 
and one of
Linear equations, matrices, determinants. Introduction to vector spaces and linear transformations and bases. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. An emphasis on applications involving matrix and vector calculations. Prerequisite: MATH 150 or 151; or MACM 101; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 240 make not take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Luis Goddyn 
Mo, We, Fr 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 
D200 
Seyyed Aliasghar Hosseini 
Mo, We, Fr 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 
OP01  TBD  
OP02  TBD 
Linear equations, matrices, determinants. Real and abstract vector spaces, subspaces and linear transformations; basis and change of basis. Complex numbers. Eigenvalues and eigenvectors; diagonalization. Inner products and orthogonality; least squares problems. Applications. Subject is presented with an abstract emphasis and includes proofs of the basic theorems. Prerequisite: MATH 150 or 151; or MACM 101; or MATH 154 or 157, both with a grade of at least B. Students with credit for MATH 232 cannot take this course for further credit. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Jonathan Jedwab 
Mo, We, Fr 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 
OP01  TBD 
* with a B grade or better
Upper Division Requirements
Students complete a total of 48 units, including all of
Linear programming modelling. The simplex method and its variants. Duality theory. Postoptimality 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. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Mahsa Faizrahnemoon 
Mo, We, Fr 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 
D101 
Tu 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D102 
Tu 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D103 
Tu 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 

D104 
Tu 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 
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). Quantitative.
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. Writing/Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Tamon Stephen 
Tu, Th 10:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 
and five of
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. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Randall Pyke Randall Pyke 
Mo, We, Fr 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby 
D102 
Th 5:30 PM – 6:20 PM 
REMOTE LEARNING, Burnaby 
Sequences and series of functions, topology of sets in Euclidean space, introduction to metric spaces, functions of several variables. Prerequisite: MATH 242 and 251. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Nilima Nigam 
Mo, We, Fr 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 
D101 
Th 12:30 PM – 1:20 PM 
REMOTE LEARNING, Burnaby 

D102 
Th 1:30 PM – 2:20 PM 
REMOTE LEARNING, Burnaby 
Model building using integer variables, computer solution, relaxations and lower bounds, heuristics and upper bounds, branch and bound algorithms, cutting plane algorithms, valid inequalities and facets, branch and cut algorithms, Lagrangian duality, column generation of algorithms, heuristics algorithms and analysis. Prerequisite: MATH 308. Quantitative.
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. Quantitative.
Review of discrete and continuous probability models and relationships between them. Exploration of conditioning and conditional expectation. Markov chains. Random walks. Continuous time processes. Poisson process. Markov processes. Gaussian processes. Prerequisite: STAT 330, or all of: STAT 285, MATH 208W, and MATH 251. Quantitative.
Section  Instructor  Day/Time  Location 

D100 
Richard Lockhart 
Mo, We, Fr 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 
D101 
We 8:30 AM – 9:20 AM 
REMOTE LEARNING, Burnaby 

D102 
Fr 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby 
and at least one of
This course is an introduction to the modelling, analysis, and computer simulation of complex systems. Topics include analytic modelling, discrete event simulation, experimental design, random number generation, and statistical analysis. Prerequisite: CMPT 225, (MACM 101 or (ENSC 251 and ENSC 252)) and STAT 270.
Section  Instructor  Day/Time  Location 

E100 
Alaa Alameldeen 
Tu 4:30 PM – 6:20 PM Fr 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby REMOTE LEARNING, Burnaby 
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.
Section  Instructor  Day/Time  Location 

D100 
Qianping Gu 
Mo, We, Fr 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 
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 
REMOTE LEARNING, Burnaby 
D101 
We 2:30 PM – 3:20 PM 
REMOTE LEARNING, Burnaby 

D102 
We 3:30 PM – 4:20 PM 
REMOTE LEARNING, Burnaby 

D103 
We 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 

D104 
Th 9:30 AM – 10:20 AM 
REMOTE LEARNING, Burnaby 

D105 
Th 10:30 AM – 11:20 AM 
REMOTE LEARNING, Burnaby 

D106 
Th 11:30 AM – 12:20 PM 
REMOTE LEARNING, Burnaby 

D107 
Th 4:30 PM – 5:20 PM 
REMOTE LEARNING, Burnaby 
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.
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.
and at least 6 additional units from the following list
The application of econometric techniques to the empirical investigation of economic issues. Prerequisite: ECON 201 or 301 and ECON (or BUEC) 333. Entry into this course requires a minimum CGPA of 3.0 or permission of the department. Quantitative.
Any upper division STAT course except for STAT 302, STAT 305, STAT 310, STAT 311, STAT 320, and STAT 403.
To complete the required 48 upper division units, students complete additional coursework, of which at least two courses must be 400level MATH or MACM courses with the possibility of substituting a 400level course from another department subject to advisor approval. Courses used to fulfil this upper division requirement cannot be used to satisfy the interdisciplinary requirement. All courses pertaining to the required 48 upper division units must be approved by the program advisor in the Department of Mathematics.
NOTE: SFU students accepted in the accelerated master’s within the Department of Mathematics may apply a maximum of 10 graduate course units, taken while completing the bachelor's degree, towards the upper division electives of the bachelor's program and the requirements of the master's degree. For more information go to: https://www.sfu.ca/gradstudies/apply/programs/acceleratedmasters.html.
Interdisciplinary Requirement
With advisor approval, students also complete at least 15 units from application areas. Application courses are chosen from ACMA, BUEC, BUS, CMPT, ECON, MACM, MATH, REM and STAT courses. Courses used to fulfil upper division requirements cannot be used to fulfil this requirement. If the operations research honours is completed as part of a second bachelor's degree, then the interdisciplinary requirement may be waived if the previous degree contains an approved major. Approvals are given individually. Those majors that are approved will not be limited to the disciplines listed above.
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 universitywide information.
WQB Graduation Requirements
A grade of C or better is required to earn W, Q or B credit
Requirement 
Units 
Notes  
W  Writing 
6 
Must include at least one upper division course, taken at Simon Fraser University within the student’s major subject  
Q  Quantitative 
6 
Q courses may be lower or upper division  
B  Breadth 
18 
Designated Breadth  Must be outside the student’s major subject, and may be lower or upper division 6 units Social Sciences: BSoc 6 units Humanities: BHum 6 units Sciences: BSci 
6 
Additional Breadth  6 units outside the student’s major subject (may or may not be Bdesignated courses, and will likely help fulfil individual degree program requirements) Students choosing to complete a joint major, joint honours, double major, two extended minors, an extended minor and a minor, or two minors may satisfy the breadth requirements (designated or not designated) with courses completed in either one or both program areas. 
Residency Requirements and Transfer Credit
 At least half of the program's total units must be earned through Simon Fraser University study.
 At least two thirds of the program's total upper division units must be earned through Simon Fraser University study.
Elective Courses
In addition to the courses listed above, students should consult an academic advisor to plan the remaining required elective courses.