Mathematics and Statistics for Management Major (B.Com.) (72-75 credits)
Offered by: Management (Desautels Faculty of Management)
Degree: Bachelor of Commerce
Program credit weight: 72-75
Program Description
(72-75 credits)
The B.Com.; Major in Mathematics and Statistics for Management focuses on newer methodologies and strategies to solve current and modern real-world problems. This program may be completed in 72 or 75 credits.
Program Prerequisites
Before entering the program, students must have completed the following courses [or their equivalent] if not already completed, above the program’s 72-75 credits.
0-10 credits from:
| Course | Title | Credits |
|---|---|---|
| MATH 133 | Linear Algebra and Geometry. | 3 |
Linear Algebra and Geometry. Terms offered: Summer 2026, Fall 2026, Winter 2027 Systems of linear equations, matrices, inverses, determinants; geometric vectors in three dimensions, dot product, cross product, lines and planes; introduction to vector spaces, linear dependence and independence, bases. Linear transformations. Eigenvalues and diagonalization. | ||
| MATH 140 | Calculus 1. | 3 |
Calculus 1. Terms offered: Summer 2026, Fall 2026, Winter 2027 Review of functions and graphs. Limits, continuity, derivative. Differentiation of elementary functions. Antidifferentiation. Applications. | ||
| MATH 141 | Calculus 2. | 4 |
Calculus 2. Terms offered: Summer 2026, Fall 2026, Winter 2027 The definite integral. Techniques of integration. Applications. Introduction to sequences and series. | ||
Required Courses (63 credits)
Management Core
| Course | Title | Credits |
|---|---|---|
| MGCR 211 | Introduction to Financial Accounting. | 3 |
Introduction to Financial Accounting. Terms offered: Summer 2026, Fall 2026, Winter 2027 The role of financial accounting in the reporting of the financial performance of a business. The principles, components and uses of financial accounting and reporting from a user's perspective, including the recording of accounting transactions and events, the examination of the elements of financial statements, the preparation of financial statements and the analysis of financial results. | ||
| MGCR 222 | Introduction to Organizational Behaviour. | 3 |
Introduction to Organizational Behaviour. Terms offered: Summer 2026, Fall 2026, Winter 2027 Individual motivation and communication style; group dynamics as related to problem solving and decision making, leadership style, work structuring and the larger environment. Interdependence of individual, group and organization task and structure. | ||
| MGCR 233 | Data Programming for Business. | 3 |
Data Programming for Business. Terms offered: Fall 2026, Winter 2027 Introduction to data programming for management students. | ||
| MGCR 250 | Expressive Analysis for Management. | 3 |
Expressive Analysis for Management. Terms offered: Fall 2026, Winter 2027 Focusing on skills with respect to analysis, writing and presentation in management. | ||
| MGCR 271 | Business Statistics. | 3 |
Business Statistics. Terms offered: Fall 2026, Winter 2027 Statistical concepts and methodology, their application to managerial decision-making, real-life data, problem-solving and spreadsheet modeling. Topics include: descriptive statistics; normal distributions, sampling distributions and estimation, hypothesis testing for one and two populations, goodness of fit, analysis of variance, simple and multiple regression. | ||
| MGCR 293 | Managerial Economics. | 3 |
Managerial Economics. Terms offered: Summer 2026, Fall 2026, Winter 2027 The course focuses on the application of economic theory to management problems and the economic foundations of marketing, finance, and production. Attention is given to the following topics: price and cost analysis; demand and supply analysis, conditions of competition. | ||
| MGCR 294 | The Firm in the Macroeconomy. | 3 |
The Firm in the Macroeconomy. Terms offered: Fall 2026, Winter 2027 Topics and tools of macroeconomics such as national accounting, the IS-LM model, the drivers of output and business cycles, and the basics of monetary policy and inflation. Emphasis on financial markets, the role of expectations, and the reasons for possible deviations from full information market efficiency. | ||
| MGCR 331 | Information Technology Management . | 3 |
Information Technology Management . Terms offered: Fall 2026, Winter 2027 Introduction to principles and concepts of information systems in organizations. Topics include information technology, transaction processing systems, decision support systems, database and systems development. Students are required to have background preparation on basic micro computer skills including spreadsheet and word-processing. | ||
| MGCR 341 | Introduction to Finance. | 3 |
Introduction to Finance. Terms offered: Summer 2026, Fall 2026, Winter 2027 An introduction to the principles, issues, and institutions of Finance. Topics include valuation, risk, capital investment, financial structure, cost of capital, working capital management, financial markets, and securities. | ||
| MGCR 352 | Principles of Marketing. | 3 |
Principles of Marketing. Terms offered: Summer 2026, Fall 2026, Winter 2027 Introduction to marketing principles, focusing on problem solving and decision making. Topics include: the marketing concept; marketing strategies; buyer behaviour; Canadian demographics; internal and external constraints; product; promotion; distribution; price. Lectures, text material and case studies. | ||
| MGCR 372 | Operations Management. | 3 |
Operations Management. Terms offered: Summer 2026, Fall 2026, Winter 2027 Design, planning, establishment, control, and improvement of the activities/processes that create a firm's final products and/or services. The interaction of operations with other business areas will also be discussed. Topics include forecasting, product and process design, waiting lines, capacity planning, inventory management and total quality management. | ||
| MGCR 382 | International Business. | 3 |
International Business. Terms offered: Fall 2026, Winter 2027 An introduction to the world of international business. Economic foundations of international trade and investment. The international trade, finance, and regulatory frameworks. Relations between international companies and nation-states, including costs and benefits of foreign investment and alternative controls and responses. Effects of local environmental characteristics on the operations of multi-national enterprises. | ||
| MGCR 423 | Strategic Management. | 3 |
Strategic Management. Terms offered: Summer 2026, Fall 2026, Winter 2027 An integrative and interdisciplinary introduction to strategy formation and execution. Concepts, tools, and practical application to understand how firms leverage resources and capabilities to gain competitive advantage in dynamic, contemporary industries. Strategic positioning, organizational design, and managerial action for the long-term success of businesses and positive social and ecological outcomes. | ||
| MGCR 460 | Social Context of Business. | 3 |
Social Context of Business. Terms offered: Fall 2026, Winter 2027 Examination of how business interacts with the larger society. Exploration of the development of modern capitalist society, and the dilemmas that organizations face in acting in a socially responsible manner. Examination of these issues with reference to sustainable development, business ethics, globalization and developing countries, and political activity. | ||
Major
| Course | Title | Credits |
|---|---|---|
| MATH 222 | Calculus 3. | 3 |
Calculus 3. Terms offered: Summer 2026, Fall 2026, Winter 2027 Taylor series, Taylor's theorem in one and several variables. Review of vector geometry. Partial differentiation, directional derivative. Extreme of functions of 2 or 3 variables. Parametric curves and arc length. Polar and spherical coordinates. Multiple integrals. | ||
| MATH 223 | Linear Algebra. | 3 |
Linear Algebra. Terms offered: Fall 2026, Winter 2027 Review of matrix algebra, determinants and systems of linear equations. Vector spaces, linear operators and their matrix representations, orthogonality. Eigenvalues and eigenvectors, diagonalization of Hermitian matrices. Applications. | ||
| MATH 242 | Analysis 1. | 3 |
Analysis 1. Terms offered: Fall 2026 A rigorous presentation of sequences and of real numbers and basic properties of continuous and differentiable functions on the real line. | ||
| MATH 243 | Analysis 2. | 3 |
Analysis 2. Terms offered: Winter 2027 Definition and properties of Riemann integral, Fundamental Theorem of Calculus, Taylor's theorem. Infinite series: alternating, telescoping series, rearrangements, conditional and absolute convergence, convergence tests. Power series and Taylor series. Elementary functions. Introduction to metric spaces. | ||
| MATH 323 | Probability. | 3 |
Probability. Terms offered: Summer 2026, Fall 2026, Winter 2027 Sample space, events, conditional probability, independence of events, Bayes' Theorem. Basic combinatorial probability, random variables, discrete and continuous univariate and multivariate distributions. Independence of random variables. Inequalities, weak law of large numbers, central limit theorem. | ||
| MATH 324 | Statistics. | 3 |
Statistics. Terms offered: Fall 2026, Winter 2027 Sampling distributions, including Normal, Chi-squared, Student’s t, and F distributions. Sampling properties of the sample mean and variance. Point estimation: bias, variance, mean squared error, consistency, sufficiency, UMVUE. Method of moments and maximum likelihood estimation. One- and two-sample inference. Confidence intervals and sample size determination. Hypothesis testing, Type 1 and Type 2 errors, power, and p-values. The Neyman Pearson framework and likelihood ratio tests. Linear models used as illustrative examples, linking them to topics in estimation and hypothesis testing. One-way analysis of variance (ANOVA) and contingency tables. | ||
| MGSC 373 | Operations Research 1. | 3 |
Operations Research 1. Terms offered: Winter 2027 A realistic experience of analytical models which have been successfully applied in several areas of managerial decision-making like marketing, finance and IS. Emphasis on the formulation of problems, their solution approaches, limitations, underlying assumptions and practical use. Topics include: decision analysis, project management, simulation, linear and integer programming, sensitivity analysis. | ||
Complementary Courses (9-12 credits)
9-12 credits from:
| Course | Title | Credits |
|---|---|---|
| COMP 202 | Foundations of Programming. | 3 |
Foundations of Programming. Terms offered: Summer 2026, Fall 2026, Winter 2027 Introduction to computer programming in a high level language: variables, expressions, primitive types, methods, conditionals, loops. Introduction to algorithms, data structures (arrays, strings), modular software design, libraries, file input/output, debugging, exception handling. Selected topics. | ||
| COMP 551 | Applied Machine Learning. | 4 |
Applied Machine Learning. Terms offered: Fall 2026, Winter 2027 Selected topics in machine learning and data mining, including clustering, neural networks, support vector machines, decision trees. Methods include feature selection and dimensionality reduction, error estimation and empirical validation, algorithm design and parallelization, and handling of large data sets. Emphasis on good methods and practices for deployment of real systems. | ||
| FINE 452 | Applied Quantitative Finance. | 3 |
Applied Quantitative Finance. Terms offered: this course is not currently offered. The course is built around a series of practical applications (backtesting trading strategies, yield curve modelling, derivatives hedging) and consists of lab sessions where lectures are mixed with time and support for solving the tasks in Matlab. No programming experience is required, but a willingness to learn is. | ||
| FINE 460 | Financial Analytics. | 3 |
Financial Analytics. Terms offered: this course is not currently offered. An extensive study of the empirical methods casually used in the different subfields of finance. Examination of the most popular statistics models used in finance, both from a theoretical and practical point of view. An important emphasis will be put on the distinction between models of financial mechanisms, and those motivated purely by efficacy. | ||
| MATH 208 | Introduction to Statistical Computing. | 3 |
Introduction to Statistical Computing. Terms offered: Fall 2026 Basic data management. Data visualization. Exploratory data analysis and descriptive statistics. Writing functions. Simulation and parallel computing. Communication data and documenting code for reproducible research. | ||
| MATH 308 | Fundamentals of Statistical Learning. | 3 |
Fundamentals of Statistical Learning. Terms offered: Winter 2027 Theory and application of various techniques for the exploration and analysis of multivariate data: principal component analysis, correspondence analysis, and other visualization and dimensionality reduction techniques; supervised and unsupervised learning; linear discriminant analysis, and clustering techniques. Data applications using appropriate software. | ||
| MATH 314 | Advanced Calculus. | 3 |
Advanced Calculus. Terms offered: Fall 2026, Winter 2027 Derivative as a matrix. Chain rule. Implicit functions. Constrained maxima and minima. Jacobians. Multiple integration. Line and surface integrals. Theorems of Green, Stokes and Gauss. Fourier series with applications. | ||
| MATH 315 | Ordinary Differential Equations. | 3 |
Ordinary Differential Equations. Terms offered: Fall 2026, Winter 2027 First order ordinary differential equations including elementary numerical methods. Linear differential equations. Laplace transforms. Series solutions. | ||
| MATH 423 | Applied Regression. | 3 |
Applied Regression. Terms offered: Fall 2026 Multiple regression estimators and their properties. Hypothesis tests and confidence intervals. Analysis of variance. Prediction and prediction intervals. Model diagnostics. Model selection. Introduction to weighted least squares. Basic contingency table analysis. Introduction to logistic and Poisson regression. Applications to experimental and observational data. | ||
| MATH 427 | Statistical Quality Control. | 3 |
Statistical Quality Control. Terms offered: this course is not currently offered. Introduction to quality management; variability and productivity. Quality measurement: capability analysis, gauge capability studies. Process control: control charts for variables and attributes. Process improvement: factorial designs, fractional replications, response surface methodology, Taguchi methods. Acceptance sampling: operating characteristic curves; single, multiple and sequential acceptance sampling plans for variables and attributes. | ||
| MATH 447 | Introduction to Stochastic Processes. | 3 |
Introduction to Stochastic Processes. Terms offered: Winter 2027 Conditional probability and conditional expectation, generating functions. Branching processes and random walk. Markov chains, transition matrices, classification of states, ergodic theorem, examples. Birth and death processes, queueing theory. | ||
| MATH 511 | Analysis of Categorical Data. | 4 |
Analysis of Categorical Data. Terms offered: Winter 2027 Probability distributions for categorical data, Analysis of 2X2 contingency tables, Multiway contingency tables, The Logistic regression, Logistic regression for categorical predictors, Logit models for nominal and ordinal responses, Log-linear models and modelling ordinal associations in contingency tables, Unsupervised learning techniques for categorical data, Non Linear Principal component analysis, Applications of unsupervised learning techniques using R, Item Response Theory, Rasch model. Some topics may be included or excluded as the time permits. | ||
| MATH 523 | Generalized Linear Models. | 4 |
Generalized Linear Models. Terms offered: Winter 2027 Exponential families, link functions. Inference and parameter estimation for generalized linear models; model selection using analysis of deviance. Residuals. Contingency table analysis, logistic regression, multinomial regression, Poisson regression, log-linear models. Multinomial models. Overdispersion and Quasilikelihood. Applications to experimental and observational data. | ||
| MATH 524 | Nonparametric Statistics. | 4 |
Nonparametric Statistics. Terms offered: this course is not currently offered. Distribution free procedures for 2-sample problem: Wilcoxon rank sum, Siegel-Tukey, Smirnov tests. Shift model: power and estimation. Single sample procedures: Sign, Wilcoxon signed rank tests. Nonparametric ANOVA: Kruskal-Wallis, Friedman tests. Association: Spearman's rank correlation, Kendall's tau. Goodness of fit: Pearson's chi-square, likelihood ratio, Kolmogorov-Smirnov tests. Statistical software packages used. | ||
| MATH 527D1 | Statistical Data Science Practicum. | 3 |
Statistical Data Science Practicum. Terms offered: Fall 2026 The holistic skills required for doing statistical data science in practice. Data science life cycle from a statistics-centric perspective and from the perspective of a statistician working in the larger data science environment. Group-based projects with industry, government, or university partners. Statistical collaboration and consulting conducted in coordination with the Data Science Solutions Hub (DaS^2H) of the Computational and Data Systems Initiative (CDSI). | ||
| MATH 527D2 | Statistical Data Science Practicum. | 3 |
Statistical Data Science Practicum. Terms offered: Winter 2027 See MATH 527D1 for course description. | ||
| MATH 545 | Introduction to Time Series Analysis. | 4 |
Introduction to Time Series Analysis. Terms offered: Winter 2027 Stationary processes; estimation and forecasting of ARMA models; non-stationary and seasonal models; state-space models; financial time series models; multivariate time series models; introduction to spectral analysis; long memory models. | ||
| MATH 558 | Design of Experiments. | 4 |
Design of Experiments. Terms offered: Winter 2027 Introduction to concepts in statistically designed experiments. Randomization and replication. Completely randomized designs. Simple linear model and analysis of variance. Introduction to blocking. Orthogonal block designs. Models and analysis for block designs. Factorial designs and their analysis. Row-column designs. Latin squares. Model and analysis for fixed row and column effects. Split-plot designs, model and analysis. Relations and operations on factors. Orthogonal factors. Orthogonal decomposition. Orthogonal plot structures. Hasse diagrams. Applications to real data and ethical issues. | ||
| MATH 559 | Bayesian Theory and Methods. | 4 |
Bayesian Theory and Methods. Terms offered: Fall 2026 Subjective probability, Bayesian statistical inference and decision making, de Finetti’s representation. Bayesian parametric methods, optimal decisions, conjugate models, methods of prior specification and elicitation, approximation methods. Hierarchical models. Computational approaches to inference, Markov chain Monte Carlo methods, Metropolis—Hastings. Nonparametric Bayesian inference. | ||
| MGSC 403 | Introduction to Logistics Management. | 3 |
Introduction to Logistics Management. Terms offered: Winter 2027 Managing logistics systems, including transportation management, facility location, procurement, distribution management, and supply chain management. | ||
| MGSC 431 | Operations and Supply Chain Analysis. | 3 |
Operations and Supply Chain Analysis. Terms offered: Winter 2027 This course introduces students to operations concepts and techniques with application to supply chain management. A supply chain consists of all activities involved in fulfilling customer demand and the management decisions have a direct impact on a firm's performance, such as operating cost, market penetration and customer service. The course covers both classical tactics and evolving new strategies to reduce cost, increase responsiveness and maintain sustainable growth in a firm's supply chain. | ||
| MGSC 434 | Topics in Operations Management 1 1 | 3 |
Topics in Operations Management 1 Terms offered: this course is not currently offered. Selected topics in operations management. | ||
- 1
MGSC 434 Topics in Operations Management 1 when the topic is relevant to this program and approved by the Mathematics program adviser.
Students must consult the rules for credits and sequencing for Statistics courses in the Desautels Faculty of Management Course Overlap section.