Program Requirements
The program provides training in statistics with a mathematical core. Taken together with the B.A.; Supplementary Minor Concentration in Statistics, these two programs constitute an equivalent of the B.Sc.; Major in Statistics program offered by the Faculty of Science. With satisfactory performance in an appropriate selection of courses, these two programs can lead to the accreditation "A.Stat" from the Statistical Society of Canada, which is regarded as the entry level requirement for a statistician practicing in Canada. Students interested in this accreditation should consult an academic adviser.
Guidelines for Course Selection
Students who received advanced standing or the CEGEP equivalent of the 100-level Math courses listed below are no longer required to take them. Whenever an exemption without credits is granted for a 200-level and above required Math course, the latter must be replaced with a complementary course chosen in consultation with a program advisor.
Students are strongly advised to complete all required courses by the end of U2.
Where appropriate, Honours courses may be substituted for equivalent courses. Students planning to pursue
graduate studies are encouraged to make such substitutions.
Required Courses (34 credits)
* Students who have taken an equivalent of MATH 203 at CEGEP or elsewhere must replace it by another course from the Complementary course list.
** Students must take MATH 204 before taking MATH 324.
-
MATH 133 Linear Algebra and Geometry (3 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Fall 2024, Winter 2025
Instructors: Macdonald, Jeremy; Ayala, Miguel; Branchereau, Romain; Giard, Antoine (Fall)
3 hours lecture, 1 hour tutorial
Prerequisite: a course in functions
Restriction(s): 1) Not open to students who have taken CEGEP objective 00UQ or equivalent. 2) Not open to students who have taken or are taking MATH 123, except by permission of the Department of Mathematics and Statistics.
-
MATH 140 Calculus 1 (3 credits)
Overview
Mathematics & Statistics (Sci) : Review of functions and graphs. Limits, continuity, derivative. Differentiation of elementary functions. Antidifferentiation. Applications.
Terms: Fall 2024, Winter 2025
Instructors: Sabok, Marcin; Trudeau, Sidney; Kalmykov, Artem (Fall)
3 hours lecture, 1 hour tutorial
Prerequisite: High School Calculus
Restriction(s): 1) Not open to students who have taken MATH139 or MATH 150 or CEGEP objective 00UN or equivalent. 2) Not open to students who have taken or are taking MATH 122, except by permission of the Department of Mathematics and Statistics.
Each Tutorial section is enrolment limited
-
MATH 141 Calculus 2 (4 credits)
Overview
Mathematics & Statistics (Sci) : The definite integral. Techniques of integration. Applications. Introduction to sequences and series.
Terms: Fall 2024, Winter 2025
Instructors: Hassan, Hazem; Trudeau, Sidney; Zlotchevski, Andrei (Fall) Trudeau, Sidney (Winter)
Restriction(s): Not open to students who have taken CEGEP objective 00UP or equivalent.
Restriction(s): Not open to students who have taken or are taking MATH 122,except by permission of the Department of Mathematics and Statistics.
Each Tutorial section is enrolment limited
-
MATH 203 Principles of Statistics 1 (3 credits) *
Overview
Mathematics & Statistics (Sci) : Examples of statistical data and the use of graphical means to summarize the data. Basic distributions arising in the natural and behavioural sciences. The logical meaning of a test of significance and a confidence interval. Tests of significance and confidence intervals in the one and two sample setting (means, variances and proportions).
Terms: Fall 2024, Winter 2025
Instructors: Stephens, David; Correa, Jose Andres (Fall) Sajjad, Alia (Winter)
No calculus prerequisites
Restriction: This course is intended for students in all disciplines. For extensive course restrictions covering statistics courses see Section 3.6.1 of the Arts and of the Science sections of the calendar regarding course overlaps.
You may not be able to receive credit for this course and other statistic courses. Be sure to check the Course Overlap section under Faculty Degree Requirements in the Arts or Science section of the Calendar. Students should consult for information regarding transfer credits for this course.
-
MATH 204 Principles of Statistics 2 (3 credits) **
Overview
Mathematics & Statistics (Sci) : The concept of degrees of freedom and the analysis of variability. Planning of experiments. Experimental designs. Polynomial and multiple regressions. Statistical computer packages (no previous computing experience is needed). General statistical procedures requiring few assumptions about the probability model.
Terms: Winter 2025
Instructors: Nadarajah, Tharshanna (Winter)
Winter
Prerequisite: MATH 203 or equivalent. No calculus prerequisites
Restriction: This course is intended for students in all disciplines. For extensive course restrictions covering statistics courses see Section 3.6.1 of the Arts and of the Science sections of the calendar regarding course overlaps.
You may not be able to receive credit for this course and other statistic courses. Be sure to check the Course Overlap section under Faculty Degree Requirements in the Arts or Science section of the Calendar.
-
MATH 208 Introduction to Statistical Computing (3 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Fall 2024
Instructors: Lee, Kiwon (Fall)
Prerequisite(s): MATH 133
-
MATH 222 Calculus 3 (3 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Fall 2024, Winter 2025
Instructors: Pym, Brent; Tageddine, Damien (Fall)
-
MATH 223 Linear Algebra (3 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Fall 2024, Winter 2025
Instructors: Elaidi, Shereen; Bellemare, Hugues (Fall) Macdonald, Jeremy (Winter)
-
MATH 242 Analysis 1 (3 credits)
Overview
Mathematics & Statistics (Sci) : A rigorous presentation of sequences and of real numbers and basic properties of continuous and differentiable functions on the real line.
Terms: Fall 2024
Instructors: Jakobson, Dmitry (Fall)
-
MATH 323 Probability (3 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Fall 2024, Winter 2025
Instructors: Sajjad, Alia (Fall) Nadarajah, Tharshanna (Winter)
-
MATH 324 Statistics (3 credits) **
Overview
Mathematics & Statistics (Sci) : Sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, contingency tables, nonparametric inference, regression, Bayesian inference.
Terms: Fall 2024, Winter 2025
Instructors: Nadarajah, Tharshanna (Fall) Asgharian, Masoud (Winter)
Fall and Winter
Prerequisite: MATH 323 or equivalent
Restriction: Not open to students who have taken or are taking MATH 357
You may not be able to receive credit for this course and other statistic courses. Be sure to check the Course Overlap section under Faculty Degree Requirements in the Arts or Science section of the Calendar.
Complementary Courses (12 credits)
* Students can take at most one of MATH 410, MATH 420, MATH 527D1/D2 and WCOM 314.
-
COMP 551 Applied Machine Learning (4 credits)
Overview
Computer Science (Sci) : 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.
Terms: Fall 2024, Winter 2025
Instructors: Pr茅mont-Schwarz, Isabeau; Rabbany, Reihaneh (Fall) Li, Yue (Winter)
-
MATH 308 Fundamentals of Statistical Learning (3 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Winter 2025
Instructors: Yang, Archer Yi (Winter)
-
MATH 410 Majors Project (3 credits) *
Overview
Mathematics & Statistics (Sci) : A supervised project.
Terms: Fall 2024, Winter 2025
Instructors: Khadra, Anmar; Nadarajah, Tharshanna; Correa, Jose Andres; Jakobson, Dmitry; Humphries, Tony; Paquette, Courtney; Sabok, Marcin; Sajjad, Alia; Khalili, Abbas (Fall) Kelome, Djivede (Winter)
Prerequisite: Students must have 21 completed credits of the required mathematics courses in their program, including all required 200 level mathematics courses.
Requires departmental approval.
-
MATH 420 Independent Study (3 credits) *
Overview
Mathematics & Statistics (Sci) : Reading projects permitting independent study under the guidance of a staff member specializing in a subject where no appropriate course is available. Arrangements must be made with an instructor and the Chair before registration.
Terms: Fall 2024, Winter 2025
Instructors: Kelome, Djivede (Fall) Kelome, Djivede (Winter)
Fall and Winter and Summer
Requires approval by the chair before registration
Please see regulations concerning Project Courses under Faculty Degree Requirements
-
MATH 423 Applied Regression (3 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Fall 2024
Instructors: Steele, Russell (Fall)
-
MATH 427 Statistical Quality Control (3 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
-
MATH 447 Introduction to Stochastic Processes (3 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Winter 2025
Instructors: Paquette, Elliot (Winter)
-
MATH 523 Generalized Linear Models (4 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Winter 2025
Instructors: Steele, Russell (Winter)
-
MATH 524 Nonparametric Statistics (4 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Fall 2024
Instructors: Genest, Christian (Fall)
-
MATH 525 Sampling Theory and Applications (4 credits)
Overview
Mathematics & Statistics (Sci) : Simple random sampling, domains, ratio and regression estimators, superpopulation models, stratified sampling, optimal stratification, cluster sampling, sampling with unequal probabilities, multistage sampling, complex surveys, nonresponse.
Terms: Winter 2025
Instructors: Dagdoug, Mohamed Mehdi (Winter)
-
MATH 527D1 Statistical Data Science
Practicum (3 credits) *
Overview
Mathematics & Statistics (Sci) : 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).
Terms: Fall 2024
Instructors: Correa, Jose Andres; Kolaczyk, Eric (Fall)
-
MATH 527D2 Statistical Data Science
Practicum (3 credits) *
Overview
Mathematics & Statistics (Sci) : See MATH 527D1 for course description.
Terms: Winter 2025
Instructors: Correa, Jose Andres; Kolaczyk, Eric (Winter)
Corequisites: MATH 423
No credit will be given for this course unless both MATH 527D1 and MATH 527D2 are successfully completed in consecutive terms
-
MATH 545 Introduction to Time Series Analysis (4 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Winter 2025
Instructors: Stephens, David (Winter)
-
MATH 556 Mathematical Statistics 1 (4 credits)
Overview
Mathematics & Statistics (Sci) : Distribution theory, stochastic models and multivariate transformations. Families of distributions including location-scale families, exponential families, convolution families, exponential dispersion models and hierarchical models. Concentration inequalities. Characteristic functions. Convergence in probability, almost surely, in Lp and in distribution. Laws of large numbers and Central Limit Theorem. Stochastic simulation.
Terms: Fall 2024
Instructors: Khalili, Abbas (Fall)
-
MATH 557 Mathematical Statistics 2 (4 credits)
Overview
Mathematics & Statistics (Sci) : Sufficiency, minimal and complete sufficiency, ancillarity. Fisher and Kullback-Leibler information. Elements of decision theory. Theory of estimation and hypothesis testing from the Bayesian and frequentist perspective. Elements of asymptotic statistics including large-sample behaviour of maximum likelihood estimators, likelihood-ratio tests, and chi-squared goodness-of-fit tests.
Terms: Winter 2025
Instructors: Genest, Christian (Winter)
-
MATH 558 Design of Experiments (4 credits)
Overview
Mathematics & Statistics (Sci) : 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.
Terms: Winter 2025
Instructors: Sajjad, Alia (Winter)
-
MATH 559 Bayesian Theory and Methods (4 credits)
Overview
Mathematics & Statistics (Sci) : Subjective probability, Bayesian statistical inference and decision making, de Finetti鈥檚 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鈥擧astings. Nonparametric Bayesian inference.
Terms: This course is not scheduled for the 2024-2025 academic year.
Instructors: There are no professors associated with this course for the 2024-2025 academic year.
-
MATH 598 Topics in Probability and Statistics (4 credits)
Overview
Mathematics & Statistics (Sci) : This course covers a topic in probability and/or statistics.
Terms: Fall 2024, Winter 2025
Instructors: Addario-Berry, Louigi Dana; Neslehova, Johanna (Fall) Asgharian, Masoud; Khalili, Abbas (Winter)
Prerequisite(s): At least 30 credits in required or complementary courses from the Honours in Probability and Statistics program including MATH 356. Additional prerequisites may be imposed by the Department of Mathematics and Statistics depending on the nature of the topic.
Restriction(s): Requires permission of the Department of Mathematics and Statistics.
-
WCOM 314 Communicating Science (3 credits) *
Overview
WCOM : Production of written and oral assignments (in English) designed to communicate scientific problems and findings to varied audiences Analysis of the disciplinary conventions of scientific discourse in terms of audience, purpose, organization, and style; comparative rhetorical analysis of academic and popular genres, including abstracts, lab reports, research papers, print and online journalism.
Terms: Fall 2024, Winter 2025
Instructors: Kubler, Kyle; Olsen, Katrina; Guesgen, Mirjam (Fall) Kubler, Kyle (Winter)
Restriction: Not open to students who have taken CCOM 314.
