Offered by:Mathematics and Statistics
Degree:Bachelor of Science
Program Requirement:
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 in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- 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.
- Terms
- Instructors
- Jeremy Macdonald
- Charles Roth
-
MATH 140
Calculus 1
3 Credits
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): Review of functions and graphs. Limits, continuity, derivative. Differentiation of elementary functions. Antidifferentiation. Applications.
Offered by: Mathematics and Statistics
- 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
- Terms
- Instructors
- Sidney Trudeau, Marcin Sabok
-
MATH 141
Calculus 2
4 Credits
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): The definite integral. Techniques of integration. Applications. Introduction to sequences and series.
Offered by: Mathematics and Statistics
- 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 in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- 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
Intro to Statistical Computing
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 222
Calculus 3
3 Credits**
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 235
Algebra 1
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- 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.
- Terms
- Instructors
- There are no professors associated with this course for the 2024-2025 academic year
-
MATH 236
Algebra 2
3 Credits***
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 242
Analysis 1
3 Credits
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): A rigorous presentation of sequences and of real numbers and basic properties of continuous and differentiable functions on the real line.
Offered by: Mathematics and Statistics
- 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 in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 323
Probability
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- 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
- Terms
- Instructors
- Alia Sajjad
- Tharshanna Nadarajah
-
MATH 324
Statistics
3 Credits*
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): Sampling distributions, point and interval estimation, hypothesis testing, analysis of variance, contingency tables, nonparametric inference, regression, Bayesian inference.
Offered by: Mathematics and Statistics
- 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.
- Terms
- Instructors
- Tharshanna Nadarajah
- Masoud Asgharian
-
MATH 423
Applied Regression
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 523
Generalized Linear Models
4 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
Complementary Courses (20-23 credits)
0-3 credits selected from:
-
MATH 203
Principles of Statistics 1
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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).
Offered by: Mathematics and Statistics
- 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 http://www.mcgill.ca/students/transfercredit for information regarding transfer credits for this course.
- Terms
- Instructors
- Jose Andres Correa, David A Stephens
- Alia Sajjad
Part I: 6 credits selected from:
* If chosen, students take either MATH 317 or COMP 350, but not both.
-
COMP 208
Computer Programming for PS&E
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Computer Science
- 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.
- Terms
- Instructors
- Michael Langer, Isabeau Prémont-Schwarz
- Isabeau Prémont-Schwarz
-
COMP 250
Intro to Computer Science
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Computer Science
- Terms
- Instructors
- Giulia Alberini
- Giulia Alberini
-
COMP 251
Algorithms and Data Structures
3 Credits
Offered in the:
- Fall
- Winter
- Summer
Computer Science (Sci): Introduction to algorithm design and analysis. Graph algorithms, greedy algorithms, data structures, dynamic programming, maximum flows.
Offered by: Computer Science
- 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.
- Terms
- Instructors
- Giulia Alberini
- David C Becerra
-
COMP 350
Numerical Computing
3 Credits*
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Computer Science
-
MATH 209
FundlsofStatclModlng&Infrnce
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 314
Advanced Calculus
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- Terms
- Instructors
- There are no professors associated with this course for the 2024-2025 academic year
-
MATH 315
Ordinary Differential Eqns
3 Credits
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): First order ordinary differential equations including elementary numerical methods. Linear differential equations. Laplace transforms. Series solutions.
Offered by: Mathematics and Statistics
- Terms
- Instructors
- Courtney Paquette
- Niky Kamran
-
MATH 316
Complex Variables
3 Credits
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): Algebra of complex numbers, Cauchy-Riemann equations, complex integral, Cauchy's theorems. Taylor and Laurent series, residue theory and applications.
Offered by: Mathematics and Statistics
-
MATH 317
Numerical Analysis
3 Credits*
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): Error analysis. Numerical solutions of equations by iteration. Interpolation. Numerical differentiation and integration. Introduction to numerical solutions of differential equations.
Offered by: Mathematics and Statistics
- Terms
- Instructors
- There are no professors associated with this course for the 2024-2025 academic year
-
MATH 326
Nonlinear Dynamics and Chaos
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 327
Matrix Numerical Analysis
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- 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 in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): Simple and compound interest, annuities certain, amortization schedules, bonds, depreciation.
Offered by: Mathematics and Statistics
- 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 in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): Discrete Mathematics and applications. Graph Theory: matchings, planarity, and colouring. Discrete probability. Combinatorics: enumeration, combinatorial techniques and proofs.
Offered by: Mathematics and Statistics
-
MATH 350
Honours Discrete Mathematics
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- 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 in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- 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 in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 430
Mathematical Finance
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 463
Convex Optimization
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
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
Fundls of Machine Learning
3 Credits+
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Computer Science
-
COMP 551
Applied Machine Learning
4 Credits+
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Computer Science
- Terms
- Instructors
- Isabeau Prémont-Schwarz, Reihaneh Rabbany
- Yue Li
-
MATH 308
Fundls of Statistical Learning
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 410
Majors Project
3 Credits*
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): A supervised project.
Offered by: Mathematics and Statistics
- 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.
- Terms
- Instructors
- Djivede A Kelome
- Djivede A Kelome
-
MATH 420
Independent Study
3 Credits*
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- Fall and Winter and Summer
- Requires approval by the chair before registration
- Please see regulations concerning Project Courses under Faculty Degree Requirements
- Terms
- Instructors
- Djivede A Kelome
- Djivede A Kelome
-
MATH 427
Statistical Quality Control
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- 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
Intro. to Stochastic Processes
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- Winter
- Prerequisite: MATH 323
- Restriction: Not open to students who have taken or are taking MATH 547.
-
MATH 462
Machine Learning
3 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- 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 in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 524
Nonparametric Statistics
4 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- Fall
- Prerequisite: MATH 324 or equivalent
- Restriction: Not open to students who have taken MATH 424
-
MATH 525
Sampling Theory & Applications
4 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
- Prerequisite: MATH 324 or equivalent
- Restriction: Not open to students who have taken MATH 425
-
MATH 527D1
Stat. Data Science Practicum
3 Credits*
Offered in the:
- Fall
- Winter
- Summer
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).
Offered by: Mathematics and Statistics
- Terms
- Instructors
- Jose Andres Correa, Eric Kolaczyk
-
MATH 527D2
Stat. Data Science Practicum
3 Credits*
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): See MATH 527D1 for course description.
Offered by: Mathematics and Statistics
- Terms
- Instructors
- Jose Andres Correa, Eric Kolaczyk
-
MATH 545
Intro to Time Series Analysis
4 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 556
Mathematical Statistics 1
4 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 557
Mathematical Statistics 2
4 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 558
Design of Experiments
4 Credits
Offered in the:
- Fall
- Winter
- Summer
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.
Offered by: Mathematics and Statistics
-
MATH 559
Bayesian Theory and Methods
4 Credits
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): 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.
Offered by: Mathematics and Statistics
- 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 & Stats
4 Credits
Offered in the:
- Fall
- Winter
- Summer
Mathematics & Statistics (Sci): This course covers a topic in probability and/or statistics.
Offered by: Mathematics and Statistics
- 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.
- Terms
- Instructors
- Louigi Dana Addario-Berry, Johanna Neslehova
- Masoud Asgharian, Abbas Khalili
-
WCOM 314
Communicating Science
3 Credits*
Offered in the:
- Fall
- Winter
- Summer
Written and Oral Communication: 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.
Offered by: McGill Writing Centre
- Restriction: Not open to students who have taken CCOM 314.
- Terms
- Instructors
- Katrina G Olsen, Kyle Kubler, Mirjam Guesgen
- Kyle Kubler