Statistical Sciences

Statistical Sciences: Introduction

Faculty Affiliation

Arts and Science

Degree Programs

Financial Insurance

MFI

Statistics

MSc

  • Fields:
    • Statistical Theory and Applications;
    • Probability
 

PhD

  • Fields:
    • Statistical Theory and Applications;
    • Probability;
    • Actuarial Science and Mathematical Finance

Overview

Statistical Sciences involves the study of random phenomena and encompasses a broad range of scientific, industrial, and social processes. As data become ubiquitous and easier to acquire, particularly on a massive scale, and computational tools become more efficient, models for data are becoming increasingly complex. The past several decades have witnessed a vast impact of statistical methods on virtually every branch of knowledge and empirical investigation.

Please visit the departmental website for details about the fields offered, the research being conducted, and the courses. The department offers substantial computing facilities and operates a statistical consulting service for the University's research community. Programs of study may involve association with other departments such as Computer Science, Economics, Engineering, Mathematics, Public Health Sciences, and the Rotman School of Management. The department maintains an active seminar series and strongly encourages graduate student participation.

Students may be interested in the Data Science concentration within the Master of Science in Applied Computing program.

Contact and Address

MFI Program

Web: www.mfi.utoronto.ca
Email: mfi.info@utoronto.ca
Telephone: (416) 978-7420

Department of Statistical Sciences
Faculty of Arts & Science, University of Toronto
Ontario Power Building, 700 University Avenue, 9th Floor
Toronto, Ontario M5G 1Z5
Canada

MSc and PhD Programs

Web: www.statistics.utoronto.ca
Email: grad.statistics@utoronto.ca
Telephone: (416) 978-8838
Fax: (416) 978-5133

Department of Statistical Sciences
Faculty of Arts & Science, University of Toronto
Ontario Power Building, 700 University Avenue, 9th Floor
Toronto, Ontario M5G 1Z5
Canada

Statistical Sciences: Graduate Faculty

Full Members

Alexander, Monica - MA, PhD
Alexander, Rohan Peter - MEc, PhD
Badescu, Andrei - BSc, MSc, DPhil
Brenner, David - BSc, MSc, PhD
Broverman, Samuel - BSc, MSc, PhD
Brown, Patrick - BA, MSc, PhD
Brunner, Jerry - BA, MA, PhD, DPhil
Craiu, Radu - BSc, MSc, PhD (Chair and Graduate Chair)
Duvenaud, David - PhD
Eadie, Gwendolyn - BS, MSc, PhD
Escobar, Michael - BS, PhD
Evans, Michael - BSc, MSc, PhD
Feuerverger, Andrey - BSc, PhD
Goldenberg, Anna - PhD
Gronsbell, Jessica - BA, PhD
Grosse, Roger - PhD
Jaimungal, Sebastian - BSc, MSc, PhD
Knight, Keith - BSc, MS, PhD
Kong, Dehan - BS, MS, PhD
Leos Barajas, Vianey - BSc, PhD
Lin, Xiaodong - BSc, MSc, MMath, PhD
Lou, Wendy - DPhil
McDunnough, Philip - BSc, MSc, PhD
Park, Jun Young - PhD
Pesenti, Silvana - BSc, MSc, PhD
Quastel, Jeremy - BSc, MS, PhD
Reid, Nancy - BM, MSc, PhD, FRSC
Rosenthal, Jeffrey - BSc, AM, PhD, FRSC
Seco, Luis - PhD
Stafford, James - BS, MS, PhD
Strug, Lisa - BS, BA, SM, PhD
Sun, Lei - BS, PhD (Associate Chair, Graduate Studies)
Urtasun, Raquel - PhD
Virag, Balint - BA, MA, PhD
Volgushev, Stanislav - MA, PhD
Wang, Linbo - BS, PhD
Williams, Joseph - PhD
Wong, Ting-Kam Leonard - BSc, MPH, PhD
Zhou, Zhou - MSc, DPhil

Members Emeriti

Andrews, David - BSc, MSc, PhD
Guttman, Irwin - BSc, MA, PhD
Srivastava, Muni - MSc, PhD

Associate Members

Bolton, Liza - BSc
Caetano, Samantha-Jo - BSc, MSc, PhD
Campbell, Kieran - PhD
Chevalier, Fanny - PhD
Erdogdu, Murat Anil - PhD
Gibbs, Alison - BSc, MSc, PhD
Moon, Nathalie - BSc, MMath, PhD
Shams, Shahriar - MA
Sue-Chee, Shivon - PhD
Taback, Nathan - BSc, MSc, PhD
White, Bethany - BSc, MMath, PhD
Willmot, Gordon - BMath, MMath, PhD
Zhang, Vicki - BScEE, MSc

Statistical Sciences: Financial Insurance MFI

Master of Financial Insurance

Program Description

The MFI is a full-time professional program based on three pillars: data science, financial mathematics, and insurance modelling. This program is appropriate for students with backgrounds in statistics, actuarial science, economics, and mathematics. Students with a quantitative background (such as physics and engineering) and sufficient statistical training are also encouraged to apply.

Minimum Admission Requirements

  • Applicants are admitted under the General Regulations of the School of Graduate Studies. Applicants must also satisfy the Department of Statistical Sciences' additional admission requirements stated below.

  • An appropriate bachelor’s degree from a recognized university in a related field such as statistics, mathematics, finance, and actuarial science, or any discipline where there is a significant quantitative component. Studies must include significant exposure to statistics, mathematics, finance, and actuarial science, including coursework in advanced calculus, computational methods, linear algebra, probability, and statistics.

  • An average grade equivalent to at least a University of Toronto B+ in the final year or over senior courses; applicants who meet the SGS grade minimum of mid-B and demonstrate exceptional ability through appropriate workplace experience will be considered.

  • Three letters of reference including two academic references, one of which should be in a quantitative discipline.

  • A curriculum vitae detailing the student’s educational background, professional experience, and skills.

  • Applicants whose primary language is not English and who graduated from a university where the language of instruction and examination was not English must demonstrate proficiency in English using one of the official methods outlined in the SGS Calendar.

  • Selected applicants may be required to attend an interview.

Admission to the program is competitive, and achievement of the minimum admission standards does not guarantee admission into the program.

Program Requirements

  • Students must successfully complete 5.5 full-course equivalents (FCEs) as follows:

    • Eight and a half required half courses (4.0 FCEs).

    • STA2546H Data Analytics in Practice (0.25 FCE).

    • Any one of Statistical Sciences’ 0.25 FCE 4000-level graduate course offerings with significant financial, insurance, or data science components, with approval of the MFI program director.

    • STA2560Y Industrial Internship, a four-month summer internship (1.0 FCE). Students must submit a project proposal to the program director and select an advisor by April 15. Students will propose a placement site to be approved by the department. The department will provide approval of the proposal by May 15. An interim report is required by July 7. Students must prepare a final written report and deliver an oral presentation on the internship project at the conclusion of the internship.

Required Courses
Fall Session
STA2503H
Applied Probability for Mathematical Finance
STA2530H
Applied Time-Series Analysis
STA2535H
Life Insurance Mathematics
STA2536H
Data Science for Risk Modelling
STA2550H+
Industrial Seminar Series
Winter Session
STA2540H
Insurance Risk Management
STA2546H Data Analytics in Practice
STA2550H+
Industrial Seminar Series
STA2551H
Finance and Insurance Case Studies
STA2570H
Numerical Methods for Finance and Insurance
STA45## [To be selected by the student with approval of the Director.]
Summer Session
STA2560Y
Industrial Internship

+ Extended course. For academic reasons, coursework is extended into session following academic session in which course is offered.

Program Length

3 sessions full-time (typical registration sequence: F/W/S)

Time Limit

3 years full-time

Statistical Sciences: Statistics MSc

Master of Science

Program Description

Students in the MSc program can conduct research in the fields of 1) Statistical Theory and Applications or 2) Probability. The program offers numerous courses in theoretical and applied aspects of Statistical Sciences, which prepare students for pursuing a PhD program or directly entering the data science workforce.

The MSc program can be taken on a full-time or part-time basis. Program requirements are the same for the full-time and part-time options.

Fields:
1) Statistical Theory and Applications;
2) Probability

Minimum Admission Requirements

  • Admission to the MSc program is competitive, and applicants are admitted under the General Regulations of the School of Graduate Studies. Admission requirements for the Statistical Theory and Applications field and the Probability field are identical. Successful applicants have:

    • An appropriate bachelor's degree from a recognized university in a related field such as statistics, actuarial science, mathematics, economics, engineering, or any discipline where there is a significant quantitative component. Studies must include significant exposure to statistics, computer science, and mathematics, including coursework in advanced calculus, computational methods, linear algebra, probability, and statistics.

    • An average grade equivalent to at least a University of Toronto mid-B in the final year or over senior courses.

    • Three letters of reference.

    • A curriculum vitae.

  • Applicants whose primary language is not English and who graduated from a university where the language of instruction and examination was not English must demonstrate proficiency in English. See General Regulations section 4.3 for requirements.

Program Requirements

  • Both the Statistical Theory and Applications field and the Probability field have the same program requirements. All programs must be approved by the Associate Chair for Graduate Studies.

  • Students must complete a total of 4.0 full-course equivalents (FCEs), of which 2.0 must be chosen from the list below:

    • STA2101H Methods of Applied Statistics I

    • STA2201H Methods of Applied Statistics II

    • STA2111H Probability Theory I

    • STA2211H Probability Theory II

    • STA2112H Mathematical Statistics I

    • STA2212H Mathematical Statistics II

  • The remaining 2.0 FCEs may be selected from:

    • Any Department of Statistical Sciences 2000-level course or higher.

    • Any 1000-level course or higher in another graduate unit at the University of Toronto with sufficient statistical, computational, probabilistic, or mathematical content.

    • One 0.5 FCE as a reading course.

    • One 0.5 FCE as a research project.

    • A maximum of 1.0 FCE from any STA 4500-level modular course (each are 0.25 FCE).

  • All programs must be approved by the Associate Chair for Graduate Studies. Students must meet with the Associate Chair to ensure that their program meets the requirements and is of sufficient depth.

  • Part-time students are limited to taking 1.0 FCE during each session. In exceptional cases, the Associate Chair for Graduate Studies may approve 1.5 FCEs in a given session.

Program Length

3 sessions full-time (typical registration sequence: F/W/S);
6 sessions part-time

Time Limit

3 years full-time;
6 years part-time

Statistical Sciences: Statistics PhD

Doctor of Philosophy

Program Description

Students in the PhD program can conduct research in the fields of 1) Statistical Theory and Applications or 2) Probability or 3) Actuarial Science and Mathematical Finance. The research conducted in the department is vast and covers a diverse set of areas in theoretical and applied aspects of Statistical Sciences. Students have the opportunity to work in multidisciplinary areas and team up with researchers in, for example, Biostatistics, Computer Science, Economics, Engineering, and the Rotman School of Management. The main purpose of the program is to prepare students for pursuing advanced research both in academia and in research institutes.

Applicants may enter the PhD program via one of two routes: 1) following completion of an appropriate master’s degree or 2) direct entry after completing an appropriate bachelor’s degree (excluding Actuarial Science and Mathematical Finance).

 

Fields:
1) Statistical Theory and Applications;
2) Probability

PhD Program

Minimum Admission Requirements

  • Admission to the PhD program is competitive, and applicants are admitted under the General Regulations of the School of Graduate Studies.

  • Applicants may be accepted with a master's degree in statistics from a recognized university with at least a B+ average. Applicants with degrees in biostatistics, computer science, economics, engineering, mathematics, physics, or any discipline where there is a significant quantitative component will be also be considered.

  • Three letters of recommendation.

  • A curriculum vitae.

  • A letter of intent or personal statement outlining goals for graduate studies.

  • Applicants whose primary language is not English and who graduated from a university where the language of instruction and examination was not English must demonstrate proficiency in English. See General Regulations section 4.3 for requirements.

Program Requirements

Course Requirements
  • During Year 1, students are required to complete the following 3.0 full-course equivalents (FCEs):

    • STA2111H Probability Theory I.

    • STA2211H Probability Theory II.

    • STA2101H Methods of Applied Statistics I.

    • STA2201H Methods of Applied Statistics II.

    • STA3000Y Advanced Theory of Statistics.

Comprehensive Examination Requirements
  • Within Years 1 and 2, students must complete a two-part comprehensive examination: 1) an in-class written comprehensive exam and 2) a research comprehensive exam.

    • Students must attempt the in-class written comprehensive by the end of Year 1. If a student fails this portion of the comprehensive exam, one further attempt will be allowed by the end of Year 2. Students who achieve A or A+ grades in all required coursework are exempt from the in-class written exam.

    • Students must attempt the research comprehensive exam by the beginning of Year 2, which includes a technical report and an oral presentation. If a student fails this portion of the comprehensive exam, one further attempt will be allowed at the end of Year 2.

    • Students must pass both the in-class written exam and the research exam to continue in the program.

Thesis Requirements

Conducting original research is the most important part of doctoral work. The thesis document must constitute significant and original contribution to the field. Students will have yearly meetings with a committee of no less than three faculty members to assess their progress. The completed thesis must be presented and defended within the Department of Statistical Sciences in addition to being presented and defended at the School of Graduate Studies.

Residency Requirements

Students must also satisfy a two-year residency requirement, whereby students must be on campus full-time and consequently in geographical proximity to be able to participate fully in the University activities associated with the program.

Program Length

4 years

Time Limit

6 years

 

PhD Program (Direct-Entry)

Minimum Admission Requirements

  • Admission to the PhD program is competitive, and applicants are admitted under the General Regulations of the School of Graduate Studies.

  • Applicants may be accepted via direct entry with a bachelor's degree in statistics from a recognized university with at least an A– average. The department also encourages applicants from biostatistics, computer science, economics, engineering, mathematics, physics, or any discipline where there is a significant quantitative component.

  • Three letters of recommendation.

  • A curriculum vitae.

  • A letter of intent or personal statement outlining goals for graduate studies.

  • Applicants whose primary language is not English and who graduated from a university where the language of instruction and examination was not English must demonstrate proficiency in English. See General Regulations section 4.3 for requirements.

Program Requirements

Course Requirements
  • During Year 1, students are required to complete the following 3.0 full-course equivalents (FCEs):

    • STA2111H Probability Theory I.

    • STA2211H Probability Theory II.

    • STA2101H Methods of Applied Statistics I.

    • STA2201H Methods of Applied Statistics II.

    • STA3000Y Advanced Theory of Statistics.

  • Students must complete an additional 2.0 FCEs at the graduate level. The additional courses must be approved by the Associate Chair of Graduate Studies.

Comprehensive Examination Requirements
  • Within Years 1 and 2, students must complete a two-part comprehensive examination: 1) an in-class written comprehensive exam and 2) a research comprehensive exam.

    • Students must attempt the in-class written comprehensive by the end of Year 1. If a student fails this portion of the comprehensive exam, one further attempt will be allowed by the end of Year 2. Students who achieve A or A+ grades in all required coursework are exempt from the in-class written exam.

    • Students must attempt the research comprehensive exam by the beginning of Year 2, which includes a technical report and an oral presentation. If a student fails this portion of the comprehensive exam, one further attempt will be allowed at the end of Year 2.

    • Students must pass both the in-class written exam and the research exam to continue in the program.

Thesis Requirements

Conducting original research is the most important part of doctoral work. The thesis document must constitute significant and original contribution to the field. Students will have yearly meetings with a committee of no less than three faculty members to assess their progress. The completed thesis must be presented and defended within the Department of Statistical Sciences in addition to being presented and defended at the School of Graduate Studies.

Residency Requirements

Students must also satisfy a three-year residency requirement, whereby students must be on campus full-time and consequently in geographical proximity to be able to participate fully in the University activities associated with the program.

Program Length

5 years

Time Limit

7 years

 

Field: Actuarial Science and Mathematical Finance

PhD Program

Minimum Admission Requirements

  • Admission to the PhD program is competitive, and applicants are admitted under the General Regulations of the School of Graduate Studies.

  • Applicants may be accepted with a master's degree in statistics from a recognized university with at least a B+ average. Applicants with degrees in biostatistics, computer science, economics, engineering, mathematics, physics, or any discipline where there is a significant quantitative component will be also be considered.

  • Three letters of recommendation.

  • A curriculum vitae.

  • A letter of intent or personal statement outlining goals for graduate studies.

  • Applicants whose primary language is not English and who graduated from a university where the language of instruction and examination was not English must demonstrate proficiency in English. See General Regulations section 4.3 for requirements.

Program Requirements

Course Requirements
  • During Year 1, students must complete the following 3.0 full-course equivalents (FCEs):

    • All of:

      • STA2111H Probability Theory I,

      • STA2211H Probability Theory II, and

      • STA2503H Applied Probability for Mathematical Finance.

    • One of:

      • STA4246H Research Topics in Mathematical Finance or

      • STA2501H Mathematical Risk Theory.

    • Either:

      • STA3000Y Advanced Theory of Statistics or

      • STA2101H Methods of Applied Statistics I and

      • STA2201H Methods of Applied Statistics II.

Comprehensive Examination Requirements
  • Within Years 1 and 2, students must complete a two-part comprehensive examination: 1) an in-class written comprehensive exam and 2) a research comprehensive exam.

    • Students must attempt the in-class written comprehensive by the end of Year 1. If a student fails this portion of the comprehensive exam, one further attempt will be allowed by the end of Year 2. Students who achieve A or A+ grades in all required coursework are exempt from the in-class written exam.

    • Students must attempt the research comprehensive exam by the beginning of Year 2, which includes a technical report and an oral presentation. If a student fails this portion of the comprehensive exam, one further attempt will be allowed at the end of Year 2.

    • Students must pass both the in-class written exam and the research exam to continue in the program.

Thesis Requirements

Conducting original research is the most important part of doctoral work. The thesis document must constitute significant and original contribution to the field. Students will have yearly meetings with a committee of no less than three faculty members to assess their progress. The completed thesis must be presented and defended within the Department of Statistical Sciences in addition to being presented and defended at the School of Graduate Studies.

Residency Requirements

Students must also satisfy a three-year residency requirement, whereby students must be on campus full-time and consequently in geographical proximity to be able to participate fully in the University activities associated with the program.

Program Length

4 years

Time Limit

6 years

Statistical Sciences: Statistics MSc, PhD Courses

The department offers a selection of courses each year from the following list with the possibility of additions. The core courses will be offered each year. Consult the department for courses offered in the current academic year.

STA1001H
Applied Regression Analysis
STA1002H
Methods of Data Analysis
STA1003H
Sample Survey Theory and its Application
STA1004H
Introduction to Experimental Design
STA1007H
Statistics for Life and Social Scientists
JAS1101H Topics in Astrostatistics
STA2005H
Applied Multivariate Analysis
STA2006H
Applied Stochastic Processes
STA2016H
Theory and Methods for Complex Spatial Data
(prerequisite: STA302H1)
STA2051H Topics in Numerical Methods in Data Science
STA2052H Statistics, Ethics, and Law
STA2080H
Fundamentals of Statistical Genetics
STA2101H
Methods of Applied Statistics I
STA2102H
Computational Techniques in Statistics
STA2104H
Statistical Methods for Machine Learning and Data Mining
STA2111H
Probability Theory I
STA2112H
Mathematical Statistics I
STA2163H Online Learning and Sequential Decision Theory
STA2201H
Methods of Applied Statistics II
STA2202H
Time Series Analysis
STA2211H
Probability Theory II
STA2212H
Mathematical Statistics II
STA2453H
Data Science Methods, Collaboration, and Communication
STA2501H
Mathematical Risk Theory
STA2502H
Stochastic Models in Investments
STA2503H
Applied Probability for Mathematical Finance
STA2505H
Credibility Theory and Simulation Methods
STA2530H
Applied Time-Series Analysis
STA2535H
Life Insurance Mathematics
STA2536H
Data Science for Risk Modelling
STA2540H
Insurance Risk Management
STA2546H Data Analytics in Practice
STA2550H+
Industrial Seminar Series
STA2551H
Finance and Insurance Case Studies
STA2555H
Information Visualization
STA2560Y
Industrial Internship
STA2570H
Numerical Methods for Finance and Insurance
STA2600H
Teaching and Learning of Statistics in Higher Education
STA2700H
Computational Inference and Graphical Models
STA3000Y
Advanced Theory of Statistics
STA3431H
Monte Carlo Methods
STA4000H, Y
Supervised Reading Project I
STA4001H, Y
Supervised Reading Project II
STA4002H
Supervised Reading Project for an Advanced Special Topic
STA4246H
Research Topics in Mathematical Finance
STA4273H
Research Topics in Statistical Machine Learning
STA4364H
Conditional Inference: Sample Space Analysis
STA4372H Foundations of Statistical Inference
STA4412H
Topics in Theoretical Statistics Modular Courses

Note: The following modular courses are each worth 0.25 full-course equivalent (FCE).

STA4500H
Statistical Dependence: Copula Models and Beyond
STA4501H
Functional Data Analysis and Related Topics
STA4502H
Topics in Stochastic Processes
STA4505H
Applied Stochastic Control: High Frequency and Algorithmic Trading
STA4506H
Non-stationary Time Series Analysis
STA4507H
Extreme Value Theory and Applications
STA4508H
Topics in Likelihood Inference
STA4509H
Insurance Risk Models I
STA4510H
Insurance Risk Models II
STA4512H
Logical Foundations of Statistical Inference
STA4514H
Modelling and Analysis of Spatially Correlated Data
STA4515H
Multiple Hypothesis Testing and its Applications
STA4516H
Topics in Probabilistic Programming
STA4517H
Foundations and Trends in Causal Inference
STA4518H
Robust Statistical Methods
(prerequisite: STA2112H or permission)
STA4519H Optimal Transport: Theory and Algorithms
(prerequisites: STA2111H and STA2211H, or permission by the instructor)
STA4522H
The Measurement of Statistical Evidence
STA4523H
Bayesian Computation with Massive Data and Intractable Likelihoods
STA4524H
Advanced Topics in Statistical Genetics
STA4525H
Demographic Methods
STA4526H Stochastic Control and Applications in Finance
STA4527H Random Matrix Theory and Its Applications
STA4528H Dependence Modelling With Application to Risk Management
STA4529H Applications of Nonstandard Analysis to Statistics and Probability Theory
STA4530H Derivatives for Institutional Investing

+ Extended course. For academic reasons, coursework is extended into session following academic session in which course is offered.