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 students with 15 units; OR actuarial science students with 15 units. Writing.

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.

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.

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.

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.

Introduction to graph theory, trees, 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.

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.

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.

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.

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.

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.

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.

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 or 155, with a minimum grade of C-; or MATH 154 or 157, with a grade of at least B. Students with credit for MATH 158 or 251 may not take this course for further credit. Quantitative.

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.

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.

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.

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.

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.

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. Students who have taken STAT 341 or STAT 360 first may not then take this course for further credit.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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-.

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 (BUS 232, STAT 201, STAT 203, STAT 205, STAT 270, STAT 271, ENSC 280, MSE 210, or SEE 241), with a minimum grade of C-.

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-.

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.

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.

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, BUS (or BUEC) 232, or STAT 270, with a minimum grade of C-; MATH 150, MATH 151, MATH 154, or MATH 157, 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.

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.

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.

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 260, STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C-. Quantitative.

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 260 and one of 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.

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 260 and one of STAT 302 or STAT 305 or STAT 350 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

Introduction to principal components, cluster analysis, and other commonly used multivariate techniques. Prerequisite: STAT 260 and one of STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

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 260 and one of 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.

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 260 and one of STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. Students with credit for ECON 484 may not take this course for further credit. Quantitative.

A course in the management of marketing research. The basics of the design, conduct, and analysis of marketing research studies. Prerequisite: BUS 232 or ECON 233 or STAT 270, and BUS 343, both with a minimum grade of C- and 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.

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.

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.

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.

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-.

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-.

Covers professional writing in computing science, including format conventions and technical reports. The basis for ethical decision-making and the methodology for reaching ethical decisions concerning computing matters will be studied. Students will survey and write research papers, and both individual and group work will be emphasized. Prerequisite: CMPT 105W and (CMPT 275 or CMPT 276), with a minimum grade of C-. Writing.

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-.

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-.

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

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.

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 minimum grade of C-. 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 minimum grade of C-. Quantitative.

Introduces the SAS statistical package. Data management; reading, editing and storing statistical data; data exploration and representation; summarizing data with tables, graphs and other statistical tools; and data simulation. Prerequisite: STAT 285 or STAT 302 or STAT 305 or ECON 333, with a minimum grade of C-. Students with credit for STAT 340 may not take STAT 342 for further credit.

Introduction to principal components, cluster analysis, and other commonly used multivariate techniques. Prerequisite: STAT 260 and one of STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. Quantitative.

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 260 and one of 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.

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 260 and one of STAT 285 or STAT 302 or STAT 305 or ECON 333 or equivalent, with a minimum grade of C-. Students with credit for ECON 484 may not take this course for further credit. Quantitative.