Preliminary Fall / Winter 2020-21 Timetable

If you are planning to enrol in a graduate course at the Department of Statistical Sciences, we recommend you read through this page carefully. Please also note, that this course schedule is subject to changes. We will post changes and updates here, so check back frequently.

Enrolment Dates

Students enrolled at our department

Registration for the Fall Session starts at 6:00 am EST on Monday, July 13, 2020 for students enroled at the Department of Statistical Sciences only.

Students from other departments

Course enrolment for students from other department will start on Monday, August 24, 2020. You will not be able to add courses before this date. To enrol in one of our graduate courses, you will have to...

If you have any additional questions, please contact our graduate student administrator.

Start and End Dates of Classes & Final Examination Period

Term Classes Start Classes End

Fall Session 2020
(cross-listed courses)

Thursday, September 10, 2020 TBD

Fall Session 2020
(graduate courses, not cross-listed)

Tuseday, September 8, 2020 TBD

Winter Session 2021
(all courses)

Monday, January 4, 2021 TBD

 

For dates regarding university closures, course drop and registration deadlines, and tuition payment deadlines, please have a look at the School of Graduate Studies sessional dates calendar.

Course List Legend

  • F = a half-year course in the first term (September – December)
  • S = a half-year course in the second term (January– April)
  • Y = a full-year course (September – April)
  • (J) indicates a a cross-listed course (a joint graduate/undergraduate course)
  • M = Monday
  • T = Tuseday
  • W = Wednesday
  • R = Thursday
  • F = Friday
  • L0101 or L0201 = 9:00 am to 5:00 pm
  • L5101 = 5:00 pm onwards

Fall / Winter 2020-21 Timetable Course Listings

The Fall / Winter 2020-21 Timetable consists of two sessions: the Fall Session 2020 and the Winter Session 2021. Please find course listings for both sessions below. You can also find a list of our graduate courses at the School of Graduate Studies page for our department.

 

Fall Session 2020: Course Listings

Course Title (Click for description) Session Section/Time Location Instructor Delivery Method

STA1001H

(STA302H1)

 

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.

Prerequisite:

  • STA238H1/STA248H1/STA255H1/STA261H1/ECO227Y1
  • CSC108H1/CSC120H1/CSC121H1/CSC148H1
  • MAT221H1(70%)/MAT223H1/MAT240H1
F
L0101: T10
L0201: T3
L0301: W2
online Synchronous

STA1003H

(STA304H1)

Design of surveys, sources of bias, randomized response surveys. Techniques of sampling; stratification, clustering, unequal probability selection. Sampling inference, estimates of population mean and variances, ratio estimation., observational data; correlation vs. causation, missing data, sources of bias.

Exclusion: STA322H1

Prerequisite: ECO220Y1/ECO227Y1/GGR270Y1 / PSY202H1/SOC300Y1/STA221H1/STA255H1/261H1/248H1

F
L0101: F9
L0201: R9-12
L0301: F2
 online
TBA
Synchronous

STA1008H

Vocabulary of data analysis, Tests of statistical significance, Principles of research design, Introduction to unix, Introduction to SAS, Elementary significance tests, Multiple regression, Factorial ANOVA, Permutation tests, Power and sample size, Random effects models, Multivariate analysis of variance, Analysis of within-cases designs (repeated measures). If time permits, Categorical data analysis.

Prerequisite: Any introductory statistics class, taught by any department.

F

L0101: T10-12; F10

TBA
TBA
Hybrid
(STA437H1)

Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and the partial multiple and canonical cor relations; multivariate analysis of variance; classification and the linear discriminant function. The use of R software should be expected.

Prerequisite: STA302H/352Y

Recommended Preparation: MAT223H/240H

F
L5101: T7
L5201: W7
online TBA Synchronous

STA2101H

Advanced topics in statistics and data analysis with emphasis on applications. Diagnostics and residuals in linear models, introductions to generalized linear models, graphical methods, additional topics such as random effects models, split plot designs, analysis of censored data, introduced as needed in the context of case studies.

Prerequisite:

  • ECO374H1/ECO375H1/STA302H1
  • STA305H1
F L0101: R12-3 TBA Reid, Nancy In-class (TBA)

STA2102H

(STA410H1)

The goal of this course is to give an overview of some of the computational methods that are useful in statistics. The rst part of the course will focus on basic algorithms, such as the Fast Fourier Transform (and related methods) and methods for generating random variables. The second part of the course will focus on numerical methods for linear algebra and optimization (for example, computing least squares estimates and maximum likelihood estimates). Along the way, you will learn some basic theory of numerical analysis (computational complexity, convergence rates of algorithms) and you will encounter some statistical methodology that you may not have seen in other courses.

Prerequisites: The nominal prerequisites for this course are MAT223H/240H, STA302H and CSC108H/120H/121H/148H these should give you the sucient background in both statistics and computer programming to handle the course material. A solid foundation in linear algebra is very useful for this course.

F L0101: M10, W10-12 online Knight, Keith Synchronous

STA2111H

STA 2111H is a course designed for Master’s and Ph.D. level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory. Specific topics to be covered include: probability measures, the extension theorem, random variables, distributions, expectations, laws of large numbers, Markov chains.

Students should have a strong undergraduate background in Real Analysis, including calculus, sequences and series, elementary set theory, and epsilon-delta proofs. Some previous exposure to undergraduate-level probability theory is also recommended.

F L0101: M1-3; W12 Online Zhou, Zhou Synchronous

STA2112H

This course is designed for graduate students in Statistics and Biostatistics.

Review of probability theory, distribution theory for normal samples, convergence of random variables, statistical models, sufficiency and ancillarity, statistical functionals,influence curves, maximum likelihood estimation, computational methods.

Prerequisite:

  • Advanced calculus (eg. MAT237)
  • Linear algebra (eg. MAT223, MAT224).
  • A previous course in probability and/or statistics is highly recommended.
F L0101: W10-12; F10 TBA TBA In-class (TBA)

STA2202H

(STA457H1)

An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods, filtering and adjustment, spectral estimation, bivariate time series models.

The course will cover the following topics:

  • Theory of stationary processes, linear processes
  • Elements of inference in time domain with applications
  • Spectral representation of stationary processes
  • Elements of inference in frequency domain with applications
  • Theory of prediction (forecasting) with applications > ARMA processes, inference and forecasting
  • Non-stationarity and seasonality, ARIMA and SARIMA processes

Further topics, time permitting: multivariate models; GARCH models; state-space models

F L5101: T5 online TBA Synchronous

STA2453H

This course is designed to provide graduate students with experience in statistical consulting. Students are active participants in research projects brought to the Statistical Consulting Service (SCS) of the Department of Statistics. The course is offered over the two sessions, fall (September-December) and winter (January-April). The overall workload is approximately equivalent to a half graduate course and students receive a half credit.

Students are not expected to have had any experience as consultants. The purpose of the course is to provide this experience so that graduates will be better able to function in such an environment when they have completed the course. The course also provides students with the opportunity to become familiar with statistical software packages such as The SAS System. There is supervision and assistance to novice consultants.

Content: There is some classroom instruction at the start of the term, an d meetings occasionally are called to discuss special topics and for students to compare experiences. Students serve as apprentice statisticians and work under the guidance of the instructor and the SCS Coordinator on individual projects. Projects are assigned to students as they come in to the SCS. There are periods of inactivity when there are no projects and other times are very busy. The pattern of work is more like that associated with a business or working environment than a traditional course. While some consideration is taken of other academic demands on students, those enrolling must be aware that work on projects may require precedence at times.

Evaluation: Students will be graded on the quality of their work as stati stical consultants. This involves the ability to do work in a timely fashion, the quality of advice provided and the quality of the presentation of advice and written work to clients.

Prerequisite: Students should have taken some applied sta tistics courses such as an undergraduate regression course. Also undergraduate courses in applied statistics, sample survey, design of experiments and time series analysis are recommended but these are not required. Also taking some of the other 2000 level applied statistics courses is recommended as this course will serve as an excellent opportunity to put the content of these courses to work.

Y L0101: T10-12 TBA TBA In-class

STA2500H

(ACT451H1)

Parametric distributions and transformations, insurance coverage modifications, limits and deductibles, models for claim frequency and severity, models for aggregate claims,stop-loss insurance, risk measures.

Prerequisite: Consult the instructor concerning necessary background for this course

F L0101: T11; R10-12 TBA Lin, Sheldon In-class (TBA)

STA2501H

Consult the instructor for further details.

Prerequisite: Consult the instructor concerning necessary background for this course

F L0101: M9-12 Online Lin, Sheldon Synchronous

STA2502H

(ACT460H1)

This course is an introduction to the stochastic models used in Finance and Actuarial Science. Students will be exposed to the basics of stochastic calculus, particularly focusing on Brownian motions and simple stochastic differential equations. The role that martingales play in the pricing of derivative instruments will be investigated. Some exotic equity derivative products will be explored together with stochastic models for interest rates.

Prerequisite:

  • Knowledge of undergraduate probability theory is necessary.
  • Knowledge of basic financial modeling (e.g., binomial trees and log-normal distributions) is useful, but not completely necessary.
F L0101: T2-5 TBA Zhang, Yuchong In-class (TBA)

STA2503H

(MMF1928H **)

This course features studies in derivative pricing theory and focuses on financial mathematics and its applications to various derivative products. A working knowledge of probability theory, stochastic calculus (see e.g., STA 2502), knowledge of ordinary and partial differential equations and familiarity with the basic financial instruments is assumed.

The tentative topics covered in this course include, but is not limited to:

  • no-arbitrage and the fundamental theorem of asset pricing,
  • binomial pricing models;
  • continuous time limits;
  • the Black-Scholes model;
  • the Greeks and hedging;
  • European, American, Asian, barrier and other path-dependent options;
  • short rate models and interest rate derivatives;
  • convertible bonds;
  • stochastic volatility and jumps;
  • volatility derivatives;
  • foreign exchange and commodity derivatives.

More information: Course Website STA 2503

Prerequisite: Knowledge of undergraduate probability theory is necessary. Knowledge of basic financial modeling (e.g., binomial trees and log-normal distributions), introductory stochastic calculus and financial products is useful, but not necessary. This course moves at a faster pace, is more advanced and contains a higher workload than STA2502, only students who are well prepared will be allowed to take this course. It is also distinct from STA 2047 which instead focuses on the mathematics of stochastic analysis.

F L0101: W2-5; M4-6 (T) TBA Jaimungal, Sebastian Hybrid

STA2555H

(CSC2537H)

In this course we will study techniques and algorithms for creating effective data visualizations based on principles from graphic design, visual art, perceptual psychology, and cognitive science.This course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems.

F L0101: T12-2 TBA Chevalier, Fanny Hybrid

STA2700H

This is a reading course primarily meant to sequentially follow a modular course offered in the Department. Its purpose is to offer further supervised study of an advanced topic covered for the ambitious student.

F L0101: T9-11; R11 TBA TBA Hybrid

STA3000Y

Please note that STA3000Y F & S can only be taken by PhD students in the Department of Statistical Sciences.

This is the Department’s core graduate course in statistical theory. It covers the basic principles of statistical inference, their application to a variety of statistical models, and some generalizations to more complex settings.

Prerequisite:

  • STA2112H and STA2212H or equivalent. (STA2111H and STA2211H may be co-requisites).
  • Some familiarity with measure theory is very useful. The text includes some supplementary material on this.
F L0101: T2-5 TBA Kong, Dehan / Wang, Linbo Hybrid

STA3431H

This course will explore Monte Carlo computer algorithms, which use randomness to perform difficult high-dimensional computations. Different types of algorithms, theoretical issues, and practical applications will all be considered. Particular emphasis will be placed on Markov chain Monte Carlo (MCMC) methods. The course will involve a combination of methodological investigations, mathematical analysis, and computer programming.

Prerequisite: Knowledge of statistical inference and probability theory at the advanced undergraduate level, and familiarity with basic computer programming techniques.

F L0101: M10-12 Online Rosenthal, Jeff Synchronous

 

Winter Session 2021: Course Listings

Course Title (Click for description) Session Section/Time Location Instructor Delivery Method

STA1001H

(STA302H1)

Introduction to data analysis with a focus on regression. Initial Examination of data. Correlation. Simple and multiple regression models using least squares. Inference for regression parameters, confidence and prediction intervals. Diagnostics and remedial measures. Interactions and dummy variables. Variable selection. Least squares estimation and inference for non-linear regression.

Prerequisite:

  • STA238H1/STA248H1/STA255H1/STA261H1/ECO227Y1
  • CSC108H1/CSC120H1/CSC121H1/CSC148H1
  • MAT221H1(70%)/MAT223H1/MAT240H1
S
L0101: T10-12; R10
L0201: T2; R1-3
TBA
TBA
TBA

STA1002H

(STA303H1)

Analysis of variance for one-and two-way layouts, logistic regression, loglinear models, longitudinal data, introduction to time series.

Prerequisite: STA1001H or equivalent

S
L0101: W12; F12-2
L0201: W3-6
TBA Bolton, Liza TBA

STA1003H

(STA304H1)

Design of surveys, sources of bias, randomized response surveys. Techniques of sampling; stratification, clustering, unequal probability selection. Sampling inference, estimates of population mean and variances, ratio estimation., observational data; correlation vs. causation, missing data, sources of bias.

Exclusion: STA322H1

Prerequisite: ECO220Y1/ECO227Y1/GGR270Y1 / PSY202H1/SOC300Y1/STA221H1/STA255H1/261H1/248H1

S
L0101: M3; W3-5
 TBA Sue-Chee, Shivon TBA

STA1004H

(STA305H1)

This cross-listed course covers a number of topics used in the design and analysis of experiments. The course is intended for students of statistics as well as students of other disciplines (eg. engineering, experimental science, etc.) who will use experimental design and analysis in their work.

The course will cover the following topics: randomization, blocking Latin squares, balanced incomplete block designs, factorial experiments, confounding and fractional replication, components of variance, orthogonal polynomials, response surface methods. Additional topics will be covered based on students’ interest as time permits.

Prerequisite: STA302H/352Y/ECO327Y/ECO357Y or permission of instructor

S
L0101: M11; W11-1
L0201: T3; R3-5
TBA Sue-Chee, Shivon TBA

STA2005H 

(STA437H1)

Practical techniques for the analysis of multivariate data; fundamental methods of data reduction with an introduction to underlying distribution theory; basic estimation and hypothesis testing for multivariate means and variances; regression coefficients; principal components and the partial multiple and canonical cor relations; multivariate analysis of variance; classification and the linear discriminant function. The use of R software should be expected.

Prerequisite: STA302H/352Y

Recommended Preparation: MAT223H/240H

S
L0101: T2-5
TBA Zhou, Zhou TBA

STA2006H

(STA447H1)

Discrete and continuous time processes with an emphasis on Markov, Gaussian and renewal processes. Martingales and further limit theorems. A variety of applications taken from some of the following areas are discussed in the context of stochastic modeling: Information Theory, Quantum Mechanics, Statistical Analyses of Stochastic Processes, Population Growth Models, Reliability, Queuing Models, Stochastic Calculus, Simulation (Monte Carlo Methods).

Prerequisite: STA347H or equivalent knowledge of probability theory; and MAT235Y/237Y or equivalent knowledge of multivariate calculus and basic real analysis.

S L5101: R6-9 TBA TBA TBA

STA2016H

(STA465H1)

Data acquisition trends in the environmental, physical and health sciences are increasingly spatial in character and novel in the sense that modern sophisticated methods are required for analysis. This course will cover different types of random spatial processes and how to incorporate them into mixed effects models for Normal and non-Normal data. Students will be trained in a variety of advanced techniques for analyzing complex spatial data and, upon completion, will be able to undertake a variety of analyses on spatially dependent data, understand which methods are appropriate for various research questions, and interpret and convey results in the light of the original questions posed.

S L0101: W12; F11-1 TBA TBA TBA

STA2104H

(STA414H1)

This course will consider topics in statistics that have played a role in the development of techniques for data mining and machine learning. We will cover linear methods for regression and classification, nonparametric regression and classification methods, generalized additive models, aspects of model inference and model selection, model averaging and tree bassed methods.

Prerequisite: either STA302H or CSC411H

S
L0101: M2-5
L5101: T6-9
TBA Erdogdu, Murat TBA

STA2201H

Advanced topics in statistics and data analysis with emphasis on applications. Diagnostics and residuals in linear models, introductions to generalized linear models, graphical methods, additional topics such as random effects models, split plot designs, analysis of censored data, introduced as needed in the context of case studies.

Prerequisite:

  • ECO374H1/ECO375H1/STA302H1
  • STA305H1
S L0101: W2-5 TBA Alexander, Monica TBA

STA2202H

(STA457H1)

An overview of methods and problems in the analysis of time series data. Topics include: descriptive methods, filtering and adjustment, spectral estimation, bivariate time series models.

The course will cover the following topics:

  • Theory of stationary processes, linear processes
  • Elements of inference in time domain with applications
  • Spectral representation of stationary processes
  • Elements of inference in frequency domain with applications
  • Theory of prediction (forecasting) with applications > ARMA processes, inference and forecasting
  • Non-stationarity and seasonality, ARIMA and SARIMA processes

Further topics, time permitting: multivariate models; GARCH models; state-space models

S L0101: R3-6 TBA Zhou, Zhou TBA

STA2211H

STA 2211H is a follow-up course to STA 2111F, designed for Master’s and Ph.D. level students in statistics, mathematics, and other departments, who are interested in a rigorous, mathematical treatment of probability theory using measure theory. Specific topics to be covered include: weak convergence, characteristic functions, central limit theorems, the Radon-Nykodym Theorem, Lebesgue Decomposition, conditional probability and expectation, martingales, and Kolmogorov’s Existence Theorem.

S L0101: R9-12 TBA Volgushev, Stanislav TBA

STA2212H

This course is a continuation of STA2112H. It is designed for graduate students in statistics and biostatistics.

Topics include:

  • Likelihood inference
  • Bayesian methods
  • Significance testing
  • Linear and generalized linear models
  • Goodness-of-fit
  • Computational methods

Prerequisite: STA2212H

S L0101: W10-12; F10 TBA Reid, Nancy TBA

STA2453H

This course is designed to provide graduate students with experience in statistical consulting. Students are active participants in research projects brought to the Statistical Consulting Service (SCS) of the Department of Statistics. The course is offered over the two sessions, fall (September-December) and winter (January-April). The overall workload is approximately equivalent to a half graduate course and students receive a half credit.

Students are not expected to have had any experience as consultants. The purpose of the course is to provide this experience so that graduates will be better able to function in such an environment when they have completed the course. The course also provides students with the opportunity to become familiar with statistical software packages such as The SAS System. There is supervision and assistance to novice consultants.

Content: There is some classroom instruction at the start of the term, an d meetings occasionally are called to discuss special topics and for students to compare experiences. Students serve as apprentice statisticians and work under the guidance of the instructor and the SCS Coordinator on individual projects. Projects are assigned to students as they come in to the SCS. There are periods of inactivity when there are no projects and other times are very busy. The pattern of work is more like that associated with a business or working environment than a traditional course. While some consideration is taken of other academic demands on students, those enrolling must be aware that work on projects may require precedence at times.

Evaluation: Students will be graded on the quality of their work as stati stical consultants. This involves the ability to do work in a timely fashion, the quality of advice provided and the quality of the presentation of advice and written work to clients.

Prerequisite: Students should have taken some applied sta tistics courses such as an undergraduate regression course. Also undergraduate courses in applied statistics, sample survey, design of experiments and time series analysis are recommended but these are not required. Also taking some of the other 2000 level applied statistics courses is recommended as this course will serve as an excellent opportunity to put the content of these courses to work.

Y L0101: T10-12 TBA TBA TBA

STA2505H

(ACT466H1)

Limited fluctuation or American credibility, on a full and partial basis. Greatest accuracy or European credibility, predictive distributions and the Bayesian premium, credibility premiums including the Buhlmann and Buhlmann-Straub models, empirical Bayes nonparametric and semi-parametric parameter estimation. Simulation, random numbers, discrete and continuous random variable generation, discrete event simulation, statistical analysis of simulated data and validation techniques.

Prerequisite: Consult the instructor concerning necessary background for this course

S L0101: T11; R10-12 TBA TBA TBA

STA3000Y

Please note that STA3000Y F & S can only be taken by PhD students in the Department of Statistical Sciences.

This is the Department’s core graduate course in statistical theory. It covers the basic principles of statistical inference, their application to a variety of statistical models, and some generalizations to more complex settings.

Prerequisite:

  • STA2112H and STA2212H or equivalent. (STA2111H and STA2211H may be co-requisites).
  • Some familiarity with measure theory is very useful. The text includes some supplementary material on this.
S L0101: W10-1 TBA Simpson, Daniel TBA

STA4246H

This course focuses on advanced theory and modeling of financial derivatives. The topics include, but are not limited to: HJM interest rate models, LFM and LSM market models; foreign exchange options; defaultable bonds; credit default swaps, equity default swaps and collateralized debt obligations; intensity and structural based models; jump processes and stochastic volatility; commodity models. As well, students are required to complete a project, write a report and present a topic of current research interest.

Prerequisite: STA 2503 or equivalent knowledge.

S L5101: M6-9 TBA Zhang, Yuchong TBA

STA4273H

This is a full semester course.

Recently, new inference methods have allowed us to train learn generative latent-variable models. These models let us generate novel images and text, find meaningful latent representations of data, take advantage of large unlabeled datasets, and even let us do analogical reasoning automatically. However, most generative models such as GANs and variational autoencoders currently have pre-specified model structure, and represent data using fixed-dimensional continuous vectors. This seminar course will develop extensions to these approaches to learn model structure, and represent data using mixed discrete and continuous data structures such as lists of vectors, graphs, or even programs. The class will have a major project component.

Prerequisites: This course is designed to bring students to the current state of the art, so that ideally, their course projects can make a novel contribution. A previous course in machine learning such as CSC321, CSC411, CSC412, STA414, or ECE521 is strongly recommended. However, the only hard requirements are linear algebra, basic multivariate calculus, basics of working with probability, and basic programming skills.

More information: Course site

S L0101: R1-3 TBA Maddison, Chris TBA
STA4522H S L0101: R10-1 (1st half) TBA TBA TBA

STA4528H

S L0101: T3-6 (2nd half) TBA Pesenti, Silvana TBA

JAS1101H

This graduate-level course provides an introduction to the cross-disciplinary field of astrostatistics, and is intended for both astronomy and statistics students. We will cover topics in statistics (e.g., hierarchical Bayesian analysis, time series analysis, and cluster analysis) in the context of their applications to astronomical research (e.g., studies of galaxies, the Milky Way, exoplanets, and stellar populations).

These topics will be covered through two main aspects of the course: 1) peer-instruction and collaboration on a term project, and 2) readings, in-class discussion, and exercises related to current astrostats literature. For the term project, the students will develop practical skills by collaborating in cross-disciplinary teams on a research project in astrostatistics using real astronomical data.

S L0101:M9:30-11; W9-10:30 TBA Eadie, Gwendolyn TBA