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General overview of universally useful concepts in insurance, pensions and financial management. Typical life, health and property & casualty insurance products; underwriting; pricing; reserving; regulation; social insurance; retirement plans and annuities; financial planning: mortgages, loans, wealth management. Students with credit for ACMA 101 may not take this course for further credit. Quantitative/Breadth-Science.

A practical introduction to the science of risk management, applicable in both personal and business contexts for making data-driven decisions under uncertainty. Topics include risk identification and classification, qualitative and quantitative risk assessment, risk mitigation through pooling and insurance, and emerging risks. Applications in finance, cybersecurity, healthcare.

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-. 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. Students with credit for either MATH 150, 154 or 157 may not take MATH 151 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.

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.

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.

Theory and methods of cost compilation for managerial planning, control and decision making; the use of budgets and analysis in planning and controlling operations, establishing supervisory and departmental responsibility, and various techniques of measuring results. Prerequisite: BUS 251 with a minimum grade of C-; 15 units. 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.

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/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.

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.

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. Quantitative.

Measurement of interest, present value. Equations of value. Annuities. Loans and amortization schedules. Bonds and other securities. Cash flows: yield rates, duration, convexity, immunization. Yield curves: spot rates, forward rates. Interest rate swaps. Prerequisite: MATH 152 with a minimum grade of C; or MATH 155 or MATH 158, with a grade of at least B. Recommended: RISK (or ACMA) 101 or RISK 102. Students with credit for ACMA 201 or ACMA 210 may not take this course for further credit. Quantitative.

Introduction to key analytical tools used to organize and process data to support decision making. Prerequisite: BC Mathematics 12 (or equivalent) or any 100-level MATH course with a minimum grade of C. Students with credit for ACMA 231 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, 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.

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 course is a continuation of STAT 270. Review of probability models. Procedures for statistical inference using survey results and experimental data. Statistical model building. Elementary design of experiments. Regression methods. Introduction to categorical data analysis. Prerequisite: STAT 270 and one of MATH 152, MATH 155, or MATH 158, all with a minimum grade of C-. Quantitative.

Investments from an individual and institutional point of view. Topics include: bond valuation and the term structure of interest rates, stock valuation, portfolio theory, asset pricing models, efficient markets and introduction to derivative securities. Prerequisite: BUS 312 and (BUS 207 or ECON 201), both with a minimum grade of C- and 45 units; OR actuarial science students with STAT 285 and (BUS 312 or ACMA 201), both with a minimum grade of C- and 45 units. Quantitative.

Basic survival models, life tables. Basic life insurance and annuities: present value random variables, premium calculations, policy values. Computer applications for pricing and reserving for life insurance portfolios. Prerequisite: One of RISK (or ACMA) 101 or RISK 102, and all of RISK (or ACMA) 201, RISK (or ACMA) 231 and STAT 270, all with a minimum grade of C. Students with credit for ACMA 301 or ACMA 320 may not take this course for further credit. Quantitative.

Short-term insurance and reinsurance coverages. Basic ratemaking and reserving for short-term insurance. Models for severity and frequency of claims. Coverage modifications: deductibles, policy limits, coinsurance. Estimation and selection of parametric models in the context of insurance. Prerequisite: RISK (or ACMA) 101 or RISK 102, with a minimum grade of C. Corequisite: STAT 330, RISK 231. Students with credit for ACMA 321 or ACMA 355 may not take this course for further credit. Quantitative.

Advanced survival models. Multiple state models. Advanced premium calculations and policy values. Profit testing. Pension plans and retirement benefits. Computer applications for pricing, reserving and risk measurement of life insurance portfolios. Prerequisite: RISK 311 (or ACMA 301 or 320), with a minimum grade of C. Students with credit for ACMA 401 or ACMA 425 may not take this course for further credit. Quantitative.

Risk measures. Extreme value theory: models and applications. Aggregate models for claims. Regression-based approaches to claims modeling: generalized linear models, linear mixed models. Prerequisite: RISK (or ACMA) 321 with a minimum grade of C. Corequisite: STAT 350. Students with credit for ACMA 421 or RISK 803 may not take this course for further credit.

Option pricing models and their application to insurance and financial risks. Discrete-time models. Black-Scholes-Merton model. Market-making, option Greeks, and hedging. Exotic options. Economic scenario generators. Prerequisite: BUS 315 and one of RISK (or ACMA) 231 or BUS 232, both with a minimum grade of C. Students with credit for ACMA 340 or RISK 801 may not take this course for further credit. Quantitative.

Review of probability and distributions. Multivariate distributions. Distributions of functions of random variables. Limiting distributions. Inference. Sufficient statistics for the exponential family. Maximum likelihood. Bayes estimation, Fisher information, limiting distributions of MLEs. Likelihood ratio tests. Prerequisite: STAT 285, MATH 251, and one of MATH 232 or MATH 240, all with a minimum grade of C-. Quantitative.

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.

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.

A practical introduction to advanced models and methods used in the valuation and management of long-term insurance coverages. Topics include cash-flow based profit analysis of modern life insurance products (universal life insurance, equity-linked insurance, and contracts with embedded options), stochastic pricing and reserving, and stochastic longevity models. Prerequisite: RISK 311 or ACMA 301, with a minimum grade of C. Corequisite: RISK 441 or ACMA 340, with a minimum grade of C. Students with credit for RISK 802 may not take this course for further credit.

Advanced ratemaking: trend analysis, ultimate losses, expense provisions, profit and contingencies, overall rate indications, classification rates. Experience rating: credibility theory and applications, bonus-malus systems. Loss reserving: estimating unpaid losses and IBNR, statistical models and assumptions for outstanding claim reserves. Prerequisite: RISK (or ACMA) 321 with a minimum grade of C. Students with credit for ACMA 422 or ACMA 470 may not take this course for further credit. Quantitative.

An introduction to basic and fundamental concepts in finance. Topics may include: financial statements analysis, time value of money, investment decisions and capital budgeting, income and equity securities and markets, risk and return, asset pricing models, capital market efficiency, capital structure. Prerequisite: BUS 254 and (BUS 232 or ECON 233 or STAT 270 or STAT 271), both with a minimum grade of C- and 45 units; OR actuarial science students with BUS 254 and (BUS 232 or ECON 233 or STAT 270 or STAT 271), both with a minimum grade of C- and 45 units. Recommended: BUS 207 or ECON 201. 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 ÓÈÎïÊÓƵ 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.

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.

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.

Guided experiences in written and oral communication of actuarial ideas and results to both expert and lay audiences. Prerequisite: RISK 311 (or ACMA 301 or 320), with a minimum grade of C. Students who have taken STAT 300W first may not then take this course for further credit. Students with credit for ACMA 360W may not take this course for further credit. Writing/Quantitative.

Topics in areas of actuarial science not covered in the regular curriculum of the department. Prerequisite: Dependent on the topics covered.

The topics included in this course will vary from term to term depending on faculty availability and student interest. Prerequisite: Dependent on the topic covered.

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.

Advanced R programming methods for data science. Tools for reproducible research. Version control. Data structures, subsetting, functions, environments, and debugging. Functional programming. Code performance: profiling, memory, integrating R and C++. Prerequisite: One of STAT 260 or STAT 341 and one of STAT 302, STAT 305, STAT 350, or ECON 333, all with a minimum grade of C-. CMPT 125 or CMPT 129 is also recommended.

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, all with a minimum grade of C-. Quantitative.

A data-first discovery of advanced statistical methods. Focus will be on a series of forecasting and prediction competitions, each based on a large real-world dataset. Additionally, practical tools for statistical modeling in real-world environments will be explored. Prerequisite: 90 units including STAT 350 with a minimum grade of C- and one of STAT 341, STAT 260, or CMPT 225, with a minimum grade of C- (STAT 240 is also recommended); OR data science majors with 90 units including STAT 302 or STAT 305, CMPT 225, STAT 260, and STAT 240, all with a minimum grade of C-.

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.

Distribution theory, methods for constructing tests, estimators, and confidence intervals with special attention to likelihood methods. Properties of the procedures including large sample theory. Prerequisite: STAT 330 with a minimum grade of C-. Quantitative.

The Bayesian approach to statistics is an alternative and increasingly popular way of quantifying uncertainty in the presence of data. This course considers comparative statistical inference, prior distributions, Bayesian computation, and applications. Prerequisite: STAT 330 and 350, 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.