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Major Statistics (54 credits)

Offered by: Mathematics and Statistics     Degree: Bachelor of Science

Program Requirements

The program provides training in statistics, with a solid mathematical core, and basic training in computing. With satisfactory performance in an appropriate selection of courses, this program can lead to the professional accreditation A. Stat from the Statistical Society of Canada, which is regarded as the entry level requirement for a Statistician practicing in Canada. The students may complete this program with 54-57 credits.

Program Prerequisites

Students entering the Major in Statistics program are normally expected to have completed the courses below or their equivalents. Otherwise they will be required to make up any deficiencies in these courses over and above the 54 credits of program courses.

  • MATH 133 Linear Algebra and Geometry (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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, Summer 2025

    Instructors: Macdonald, Jeremy; Ayala, Miguel; Branchereau, Romain; Giard, Antoine (Fall) Pinet, Th茅o (Winter)

    • 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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Review of functions and graphs. Limits, continuity, derivative. Differentiation of elementary functions. Antidifferentiation. Applications.

    Terms: Fall 2024, Winter 2025, Summer 2025

    Instructors: Sabok, Marcin; Trudeau, Sidney; Kalmykov, Artem (Fall) Huang, Peiyuan; Trudeau, Sidney (Winter)

    • 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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : The definite integral. Techniques of integration. Applications. Introduction to sequences and series.

    Terms: Fall 2024, Winter 2025, Summer 2025

    Instructors: Hassan, Hazem; Trudeau, Sidney; Zlotchevski, Andrei (Fall) Trudeau, Sidney; Poulin, Antoine; Syroka, Bartosz (Winter)

    • Prerequisites: MATH 139 or MATH 140 or MATH 150.

    • 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

In addition, a student that has not completed the equivalent of MATH 203 upon entering the program must consult an academic adviser. If a student is advised to take MATH 203, this course has to be taken as a complementary course in the first semester, increasing the total number of program credits from 54 to 57.

Students are strongly advised to complete all required courses and all Part I complementary courses by the end of U2, except for MATH 423 and MATH 523.

Students interested in the professional accreditation should consult an academic adviser.

Where appropriate, Honours courses may be substituted for equivalent Major courses. Students planning to pursue graduate studies are encouraged to make such substitutions, and to take MATH 556 and MATH 557 as complementary courses.

Required Courses (34 credits)

* Students must take MATH 204 before taking MATH 324.

** Students who have successfully completed a course equivalent to MATH 222 with a grade of C or better may omit MATH 222, but must replace it with MATH 314.
*** MATH 236 is an equivalent prerequisite to MATH 223 for required and complementary Computer Science courses listed below.

  • MATH 204 Principles of Statistics 2 (3 credits) *

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

  • MATH 222 Calculus 3 (3 credits) **

    Offered by: Mathematics and Statistics (Faculty of Science)

    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, Summer 2025

    Instructors: Pym, Brent; Tageddine, Damien (Fall) Mazakian, Hovsep (Winter)

  • MATH 235 Algebra 1 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Sets, functions and relations. Methods of proof. Complex numbers. Divisibility theory for integers and modular arithmetic. Divisibility theory for polynomials. Rings, ideals and quotient rings. Fields and construction of fields from polynomial rings. Groups, subgroups and cosets; homomorphisms and quotient groups.

    Terms: Fall 2024

    Instructors: Sabbagh, Magid (Fall)

    • Fall

    • 3 hours lecture; 1 hour tutorial

    • Prerequisite: MATH 133 or equivalent

    • Restrictions: Not open to students who have taken or are taking MATH 245.

  • MATH 236 Algebra 2 (3 credits) ***

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Linear equations over a field. Introduction to vector spaces. Linear mappings. Matrix representation of linear mappings. Determinants. Eigenvectors and eigenvalues. Diagonalizable operators. Cayley-Hamilton theorem. Bilinear and quadratic forms. Inner product spaces, orthogonal diagonalization of symmetric matrices. Canonical forms.

    Terms: Winter 2025

    Instructors: Macdonald, Jeremy (Winter)

  • MATH 242 Analysis 1 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    • Fall

    • Prerequisite: MATH 141

    • Restriction(s): Not open to students who are taking or who have taken MATH 254.

  • MATH 243 Analysis 2 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : 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.

    Terms: Winter 2025

    Instructors: Hundemer, Axel (Winter)

  • MATH 323 Probability (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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, Summer 2025

    Instructors: Sajjad, Alia (Fall) Nadarajah, Tharshanna (Winter)

    • Prerequisites: MATH 141 or equivalent.

    • Restriction: Intended for students in Science, Engineering and related disciplines, who have had differential and integral calculus

    • Restriction: Not open to students who have taken or are taking MATH 356

  • MATH 324 Statistics (3 credits) *

    Offered by: Mathematics and Statistics (Faculty of Science)

    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.

  • MATH 423 Applied Regression (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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 523 Generalized Linear Models (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

Complementary Courses (20-23 credits)

0-3 credits selected from:

  • MATH 203 Principles of Statistics 1 (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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, Summer 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.

Part I: 6 credits selected from:

* If chosen, students take either MATH 317 or COMP 350, but not both.

  • COMP 208 Computer Programming for Physical Sciences and Engineering (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Programming and problem solving in a high level computer language: variables, expressions, types, functions, conditionals, loops, objects and classes. Introduction to algorithms such as searching and sorting. Modular software design, libraries, file input and output, debugging. Emphasis on applications in Physical Sciences and Engineering, such as root finding, numerical integration, diffusion, Monte Carlo methods.

    Terms: Fall 2024, Winter 2025

    Instructors: Langer, Michael; Pr茅mont-Schwarz, Isabeau (Fall) Pr茅mont-Schwarz, Isabeau; Zammar, Chad (Winter)

    • 3 hours

    • Corequisite: MATH 133 and MATH 141, or equivalents.

    • Restrictions: Not open to students who have taken or are taking COMP 202, COMP 204, orGEOG 333; not open to students who have taken or are taking COMP 206 or COMP 250.

    • COMP 202 is intended as a general introductory course, while COMP 208 is intended for students with sufficient math background and in (non-life) science or engineering fields.

  • COMP 250 Introduction to Computer Science (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Mathematical tools (binary numbers, induction,recurrence relations, asymptotic complexity,establishing correctness of programs). Datastructures (arrays, stacks, queues, linked lists,trees, binary trees, binary search trees, heaps,hash tables). Recursive and non-recursivealgorithms (searching and sorting, tree andgraph traversal). Abstract data types. Objectoriented programming in Java (classes andobjects, interfaces, inheritance). Selected topics.

    Terms: Fall 2024, Winter 2025

    Instructors: Alberini, Giulia (Fall) Alberini, Giulia (Winter)

  • COMP 251 Algorithms and Data Structures (3 credits)

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Introduction to algorithm design and analysis. Graph algorithms, greedy algorithms, data structures, dynamic programming, maximum flows.

    Terms: Fall 2024, Winter 2025

    Instructors: Alberini, Giulia; Henderson, William (Fall) Becerra, David (Winter)

    • 3 hours

    • Prerequisites: COMP 250; MATH 235 or MATH 240

    • COMP 251 uses basic counting techniques (permutations and combinations) that are covered in MATH 240 but not in MATH 235. These techniques will be reviewed for the benefit of MATH 235 students.

    • Restrictions: Not open to students who have taken or are taking COMP 252.

  • COMP 350 Numerical Computing (3 credits) *

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Computer representation of numbers, IEEE Standard for Floating Point Representation, computer arithmetic and rounding errors. Numerical stability. Matrix computations and software systems. Polynomial interpolation. Least-squares approximation. Iterative methods for solving a nonlinear equation. Discretization methods for integration and differential equations.

    Terms: Fall 2024

    Instructors: Chang, Xiao-Wen (Fall)

  • MATH 209 Fundamentals of Statistical Modeling and Inference (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Introduction to statistical modelling, likelihood principle and maximum likelihood estimation, Bayesian principle and Bayesian estimation, with emphasis on their application in statistical analysis and data science.

    Terms: Winter 2025

    Instructors: Lee, Kiwon (Winter)

  • MATH 314 Advanced Calculus (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : 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.

    Terms: Fall 2024, Winter 2025

    Instructors: Martine, Gabriel (Fall) Borthwick, Jack Anthony (Winter)

  • MATH 315 Ordinary Differential Equations (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : First order ordinary differential equations including elementary numerical methods. Linear differential equations. Laplace transforms. Series solutions.

    Terms: Fall 2024, Winter 2025

    Instructors: Paquette, Courtney (Fall) Kamran, Niky (Winter)

    • Prerequisite: MATH 222.

    • Corequisite: MATH 133.

    • Restriction: Not open to students who have taken or are taking MATH 325.

  • MATH 316 Complex Variables (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Algebra of complex numbers, Cauchy-Riemann equations, complex integral, Cauchy's theorems. Taylor and Laurent series, residue theory and applications.

    Terms: Fall 2024

    Instructors: Kamran, Niky (Fall)

  • MATH 317 Numerical Analysis (3 credits) *

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Error analysis. Numerical solutions of equations by iteration. Interpolation. Numerical differentiation and integration. Introduction to numerical solutions of differential equations.

    Terms: Fall 2024

    Instructors: Duchesne, Gabriel William (Fall)

  • MATH 326 Nonlinear Dynamics and Chaos (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Linear systems of differential equations, linear stability theory. Nonlinear systems: existence and uniqueness, numerical methods, one and two dimensional flows, phase space, limit cycles, Poincare-Bendixson theorem, bifurcations, Hopf bifurcation, the Lorenz equations and chaos.

    Terms: Fall 2024

    Instructors: Humphries, Tony (Fall)

  • MATH 327 Matrix Numerical Analysis (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : An overview of numerical methods for linear algebra applications and their analysis. Problem classes include linear systems, least squares problems and eigenvalue problems.

    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 329 Theory of Interest (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Simple and compound interest, annuities certain, amortization schedules, bonds, depreciation.

    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 340 Discrete Mathematics (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Discrete Mathematics and applications. Graph Theory: matchings, planarity, and colouring. Discrete probability. Combinatorics: enumeration, combinatorial techniques and proofs.

    Terms: Winter 2025

    Instructors: Norin, Sergey (Winter)

  • MATH 350 Honours Discrete Mathematics (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Discrete mathematics. Graph Theory: matching theory, connectivity, planarity, and colouring; graph minors and extremal graph theory. Combinatorics: combinatorial methods, enumerative and algebraic combinatorics, discrete probability.

    Terms: Fall 2024

    Instructors: Norin, Sergey (Fall)

    • Prerequisites: MATH 235 or MATH 240 and MATH 251 or MATH 223.

    • Restrictions: Not open to students who have taken or are taking MATH 340. Intended for students in mathematics or computer science honours programs.

    • Intended for students in mathematics or computer science honours programs.

  • MATH 378 Nonlinear Optimization (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Optimization terminology. Convexity. First- and second-order optimality conditions for unconstrained problems. Numerical methods for unconstrained optimization: Gradient methods, Newton-type methods, conjugate gradient methods, trust-region methods. Least squares problems (linear + nonlinear). Optimality conditions for smooth constrained optimization problems (KKT theory). Lagrangian duality. Augmented Lagrangian methods. Active-set method for quadratic programming. SQP methods.

    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 417 Linear Optimization (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : An introduction to linear optimization and its applications: Duality theory, fundamental theorem, sensitivity analysis, convexity, simplex algorithm, interior-point methods, quadratic optimization, applications in game theory.

    Terms: Fall 2024

    Instructors: Hoheisel, Tim (Fall)

  • MATH 430 Mathematical Finance (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Introduction to concepts of price and hedge derivative securities. The following concepts will be studied in both concrete and continuous time: filtrations, martingales, the change of measure technique, hedging, pricing, absence of arbitrage opportunities and the Fundamental Theorem of Asset Pricing.

    Terms: Winter 2025

    Instructors: Kelome, Djivede (Winter)

  • MATH 463 Convex Optimization (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Introduction to convex analysis and convex optimization: Convex sets and functions, subdifferential calculus, conjugate functions, Fenchel duality, proximal calculus. Subgradient methods, proximal-based methods. Conditional gradient method, ADMM. Applications including data classification, network-flow problems, image processing, convex feasibility problems, DC optimization, sparse optimization, and compressed sensing.

    Terms: Winter 2025

    Instructors: Paquette, Courtney (Winter)

Part II: 14 credits selected from:

* If chosen, students can at most one of MATH 410, MATH 420, MATH 527D1/D2, and WCOM 314.

+ If chosen, students can take either COMP 451 or COMP 551, but not both.

  • COMP 451 Fundamentals of Machine Learning (3 credits) +

    Offered by: Computer Science (Faculty of Science)

    Overview

    Computer Science (Sci) : Introduction to the computational, statistical and mathematical foundations of machine learning. Algorithms for both supervised learning and unsupervised learning. Maximum likelihood estimation, neural networks, and regularization.

    Terms: Fall 2024

    Instructors: Ravanbakhsh, Siamak (Fall)

  • COMP 551 Applied Machine Learning (4 credits) +

    Offered by: Computer Science (Faculty of Science)

    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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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) *

    Offered by: Mathematics and Statistics (Faculty of Science)

    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) *

    Offered by: Mathematics and Statistics (Faculty of Science)

    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) Choksi, Rustum (Winter)

    • Fall and Winter and Summer

    • Requires approval by the chair before registration

    • Please see regulations concerning Project Courses under Faculty Degree Requirements

  • MATH 427 Statistical Quality Control (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    • Winter

    • Prerequisite: MATH 323

    • Restriction: Not open to students who have taken or are taking MATH 547.

  • MATH 462 Machine Learning (3 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Introduction to supervised learning: decision trees, nearest neighbors, linear models, neural networks. Probabilistic learning: logistic regression, Bayesian methods, naive Bayes. Classification with linear models and convex losses. Unsupervised learning: PCA, k-means, encoders, and decoders. Statistical learning theory: PAC learning and VC dimension. Training models with gradient descent and stochastic gradient descent. Deep neural networks. Selected topics chosen from: generative models, feature representation learning, computer vision.

    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 510 Quantitative Risk Management (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : Basics concepts in quantitative risk management: types of financial risk, loss distribution, risk measures, regulatory framework. Empirical properties of financial data, models for stochastic volatility. Extreme-value theory models for maxima and threshold exceedances. Multivariate models, copulas, and dependence measures. Risk aggregation.

    Terms: Winter 2025

    Instructors: Neslehova, Johanna (Winter)

  • MATH 524 Nonparametric Statistics (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    • Fall

    • Prerequisite: MATH 324 or equivalent

    • Restriction: Not open to students who have taken MATH 424

  • MATH 525 Sampling Theory and Applications (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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, Mehdi (Winter)

    • Prerequisite: MATH 324 or equivalent

    • Restriction: Not open to students who have taken MATH 425

  • MATH 527D1 Statistical Data Science Practicum (3 credits) *

    Offered by: Mathematics and Statistics (Faculty of Science)

    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) *

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : See MATH 527D1 for course description.

    Terms: Winter 2025

    Instructors: Correa, Jose Andres; Kolaczyk, Eric (Winter)

  • MATH 545 Introduction to Time Series Analysis (4 credits)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    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)

    Offered by: Mathematics and Statistics (Faculty of Science)

    Overview

    Mathematics & Statistics (Sci) : This course covers a topic in probability and/or statistics.

    Terms: Fall 2024, Winter 2025

    Instructors: Addario-Berry, Louigi; 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) *

    Offered by: 海外直播b站 Writing Centre (Faculty of Arts)

    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) Hardin, Katherine; Kubler, Kyle (Winter)

    • Restriction: Not open to students who have taken CCOM 314.

Faculty of Science—2024-2025 (last updated Aug. 21, 2024) (disclaimer)
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