911³Ô¹Ï

The principal elements of theory concerning utility and value, price and costs, factor analysis, productivity, labor organization, competition and monopoly, and the theory of the firm. Quantitative.

Focused on basic competencies in microeconomics, this course is suitable for business and other students not intending to specialize in economics. Topics include gains from trade, supply and demand, prices, competition and monopoly, market failures, and government policies. Economic literacy is an important part of the course. Students who have taken ECON 103 first may not then take this course for further credit. Quantitative/Breadth-Soc.

The principal elements of theory concerning money and income, distribution, social accounts, public finance, international trade, comparative systems, and development and growth. Quantitative/Breadth-Soc.

Focused on basic competencies in macroeconomics, this course is suitable for business and other students not intending to specialize in economics. Topics include GDP, economic growth, business cycles, unemployment, inflation, money, monetary and fiscal policies, exchange rates, government debt, globalization and trade policy. Economic literacy is an important part of the course. Students who have taken ECON 105 first may not then take this course for further credit. Quantitative/Breadth-Soc.

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 MATH 110 with a grade of at least A-. 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 MATH 110 with a grade of at least A. 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 MATH 110 with a grade of at least C-. 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 MATH 110 with a grade of at least C. 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 MATH 151, with a minimum grade of C-; or MATH 154 or MATH 157, with a minimum grade of B. Students with credit for MATH 155 or MATH 158 may not take this course for further credit. Quantitative.

Designed for students specializing in the life sciences. Topics include: vectors and matrices, partial derivatives, multi-dimensional integrals, systems of differential equations, compartment models, graphs and networks, and their applications to the life sciences; mathematical models of multi-component biological processes and their implementation and analysis using software. Students planning to take MATH 251 are recommended to consider MATH 152 as the prerequisite pathway. See an advisor to determine what may be best for you. Prerequisite: MATH 150, 151 or 154, with a minimum grade of C-; or MATH 157 with a grade of at least B. Students with credit for MATH 152 or 158 may not take this course for further credit. Quantitative.

Designed for students specializing in business or the social sciences. Topics include: theory of integration, integration techniques, applications of integration; functions of several variables with emphasis on double and triple integrals and their applications; introduction to differential equations with emphasis on some special first-order equations and their applications; sequences and series. Prerequisite: MATH 150 or 151 or 154 or 157, with a minimum grade of C-. Students with credit for MATH 152 or 155 may not take MATH 158 for further credit. Quantitative.

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.

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, with a minimum grade of C-). 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/Breadth-Science.

An introduction to software development for students in engineering and related programs covering theory and practicum of software design, testing, and debugging. This includes basic C++ programming language elements such as basic types, variables, expressions, statements, exception handling, functions, simple classes, and an introduction to the C++ standard library. Prerequisite: BC Math 12 (or equivalent, or any of MATH 100, 150, 151, 154, or 157, with a minimum grade of C-). Students who have taken ENSC 251, CMPT 125, 129, 135, or CMPT 200 or higher first may not then take this course for further credit.

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.

A second course in systems-oriented programming and computing science that builds upon the foundation set in CMPT 130 using a systems-oriented language such as C or C++. Topics: a review of the basic elements of programming; introduction to object-oriented 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 with a minimum grade of C-. Students with credit for CMPT 125, 126, or 129 may not take this course for further credit. 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, STAT 271, 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. Students with credit for STAT 260 may not take this course for further credit.

Introduction to MATLAB and its use in engineering. Implementation, verification, and analysis of various engineering algorithms used in signal and image processing, robotics, communications engineering. Prerequisite: ENSC 151 and (MATH 151 or MATH 150), all with a minimum grade of C-. Corequisite: MATH 152 and MATH 232.

Fundamentals for designing and implementing modular programs using a modern object-oriented programming language with a focus on understanding the performance implications of design choices on non-traditional computing platforms. Lecture topics include: classes; objects; debugging, testing & verification; design analysis & abstraction; error handling; fundamental data structures such as lists, trees, and graphs; and big-0 complexity analysis. Prerequisite: ENSC 151 with a minimum grade of C-.

Delve into critical topics such as code version control, multi-file project build systems, unit and integration testing, advanced C-programming topics including pointers, data structures, memory management, design patterns, device drivers, and real-world case studies. Prerequisite: CMPT 130.

Aspects of microeconomic theory involving competitive markets. Topics include the behavior of households and firms, partial equilibrium analysis of product and factor markets, and general equilibrium. Prerequisite: ECON 103 with a minimum grade of C- or ECON 113 with a minimum grade of B-; ECON 105 with a minimum grade of C- or ECON 115 with a minimum grade of B-; MATH 150, MATH 151, MATH 154, or MATH 157, with a minimum grade of C-. Quantitative.

Examine and review today's global economy through critical analysis of differing perspectives. Develop techniques and strategies for clear and effective written and oral communication of economic ideas. Improve critical and analytical thinking via the application of core principles to news stories or other economic data in writing and presentations. Prerequisite: ECON 201 with a minimum grade of C-, minimum 30 units and no more than 80 units. Corequisite: ECON 201, with a minimum grade of C-. ECON 201 may be taken prior to or concurrently with ECON 220W. Writing.

Rectangular, cylindrical and spherical coordinates. Vectors, lines, planes, cylinders, quadric surfaces. Vector functions, curves, motion in space. Differential and integral calculus of several variables. Vector fields, line integrals, fundamental theorem for line integrals, Green's theorem. Prerequisite: MATH 152 with a minimum grade of C-; or MATH 158 with a grade of at least B. Also, for students in the life sciences, MATH 154 with a minimum grade of C- and MATH 155 with a minimum grade of A-. Recommended: It is recommended that MATH 240 or 232 be taken before or concurrently with MATH 251.

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.

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 are advised to have taken MATH 141. Students with credit for MATH 232 cannot take this course for further credit.

Introduces statistical methods, concepts and their application to economic data using both spreadsheets (e.g., Excel) and a specialized statistical programming language such as R. Prerequisite: MATH 150, MATH 151, MATH 154, or MATH 157, with a minimum grade of C-; 15 units. MATH 150, MATH 151, MATH 154, or MATH 157 may be taken concurrently with ECON 233. Students who have taken ECON 333 first may not then take this course for further credit. STAT 270, STAT 271 or BUS 232 will be accepted in lieu of this course.

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.

This is an introductory course in probability and statistics that is designed for Computer Science students. Mainly covers basic probability theory and statistical methods for designing and analyzing computing algorithms and systems. Topics include continuous probability distributions, random variables, multivariate normal distributions, parameter estimation and inference theory, as well as design and analysis of statistical studies, including hypothesis testing and presentation of statistical data. Prerequisite: CMPT 210 with a minimum grade of C-.

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.

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.

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, STAT 271, 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. Students with credit for STAT 260 may not take this course for further credit.

Introduction to graph theory, trees, induction, automata theory, formal reasoning, modular arithmetic. Prerequisite: BC Pre-Calculus 12 or BC Calculus 12 (or equivalent), or any of MATH 100, 150, 151, 154, 157.

A continuation of MACM 101. Topics covered include graph theory, trees, inclusion-exclusion, generating functions, recurrence relations, and optimization and matching. Prerequisite: MATH 141 or MACM 101 or (ENSC 251 and one of MATH 232 or MATH 240).

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.

Using a mathematical software package for doing computations from calculus. Development of computer models that analyze and illustrate applications of calculus. All calculations and experiments will be done in the Maple software package. Topics include: graphing functions and data, preparing visual aids for illustrating mathematical concepts, integration, Taylor series, numerical approximation methods, 3D visualization of curves and surfaces, multi-dimensional optimization, differential equations and disease spread models. Prerequisite: One of CMPT 102, 120, 126, 128 or 130 and one of MATH 152, 155, or 158.

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.

Mathematical induction. Limits of real sequences and real functions. Continuity and its consequences. The mean value theorem. The fundamental theorem of calculus. Series. Prerequisite: MATH 152 with a minimum grade of C-; or MATH 155 or 158 with a grade of B. Students are advised to have taken MATH 141.

First-order differential equations, second- and higher-order linear equations, series solutions, introduction to Laplace transform, systems and numerical methods, applications in the physical, biological and social sciences. Prerequisite: MATH 152 with a minimum grade of C-; or MATH 155 or 158, with a grade of at least B; MATH 232 or 240, with a minimum grade of C-. Students with credit for MATH 310 may not take this course for further credit.

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.

Aspects of microeconomic theory concerned with strategic behavior, imperfect information, and market failure. Topics include game theory and oligopoly; uncertainty and insurance; asymmetric information and market power, externalities and public goods, together with related issues in welfare economics. Prerequisite: ECON 201 with a minimum grade of C-; 45 units.

Concepts and methods of analysis of macroeconomic variables -- consumption, investment, government and foreign trade. Classical and Keynesian models compared; analysis of economic statics and dynamics. Prerequisite: ECON 201 with a minimum grade of C-; 45 units. Students with a minimum grade of A- in ECON 103 and 105 at 911³Ô¹Ï at their first attempt can complete ECON 305 concurrently with ECON 201 after 30 units. Students seeking permission to enroll on this basis must contact the undergraduate advisor in economics.

Computational methods for solving and analyzing economic models. Topics include solving systems of equations, optimization, computing equilibria of economic models, computing dynamic and stochastic models, and data-driven estimation. Students will apply these methods to conduct economic analysis using a general-purpose programming language like Python. Prerequisite: ECON 201 and one of ECON 233, STAT 270, or STAT 271. Recommended: ECON 305.

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 B-; ECON 233, BUS (or BUEC) 232, STAT 270, or STAT 271, with a minimum grade of C-; MATH 150, MATH 151, MATH 154, or MATH 157, with a minimum grade of C-; 45 units. Students with a minimum grade of A- in ECON 233, BUS (or BUEC) 232, STAT 270, or STAT 271 can take ECON 333 after 30 units. Students seeking permission to enroll based on their ECON 233, BUS (or BUEC) 232, STAT 270, or STAT 271 grade must contact the undergraduate advisor in economics. Students with credit for BUEC 333 may not take this course for further credit.

The application of econometric techniques to the empirical investigation of economic issues. Prerequisite: ECON 201, ECON (or BUEC) 333, and one of (ECON 331, MATH 232, or MATH 240), all with a minimum grade of C-. Entry into this course requires a minimum CGPA of 3.0 or permission of the department.

Explores how to recognize and learn from patterns in data using modern statistical software for the purpose of economic analysis. Introduces students to techniques for managing, visualizing, and analyzing data to answer real-world economic questions. Prerequisite: ECON 233, BUS 232, STAT 203, STAT 270, STAT 271 or POL 201, with a minimum grade of C-; and three units of ECON or SDA 100, with a minimum grade of C-. Students with credit for POL 390, STAT 341, or STAT 452 may not take this course for further credit. Students with credit for ECON 387 under the title "Applied Data Analysis" may not take this course for further credit.

Provides an introduction to statistical methods used to analyze causal questions and evaluate policies. Discusses various approaches to drawing causal inferences from observational data. Corequisite: ECON 333, STAT 285, STAT 302, or POL 315, with a minimum grade of C-. ECON 333, STAT 285, STAT 302, and POL 315 may be taken prior to or concurrently with ECON 335.