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.
Contact and Address
MFI Program
Web: www.mfi.utoronto.ca
Email: info@mfi.utoronto.ca
Telephone: (416) 9787420
Department of Statistical Sciences
Faculty of Arts & Science, University of Toronto
Stewart Building, Room 410C, 149 College Street
Toronto, Ontario M5T 1P5
Canada
MSc and PhD Programs
Web: www.statistics.utoronto.ca
Email: grad.statistics@utstat.utoronto.ca
Telephone: (416) 9788838
Fax: (416) 9785133
Department of Statistical Sciences
Faculty of Arts & Science, University of Toronto
Sidney Smith Hall, Room 6022, 100 St. George Street
Toronto, Ontario M5S 3G3
Canada
Statistical Sciences: Graduate Faculty
Full Members
Members Emeriti
Associate Members
Statistical Sciences: Financial Insurance MFI
Master of Financial Insurance
Program Description
The MFI is a fulltime 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 midB 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 fullcourse equivalents (FCEs) as follows:

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

STA 2546H Data Analytics in Practice (0.25 FCE).

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

STA 2560Y Industrial Internship, a fourmonth 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
STA 2503H

Applied Probability for Mathematical Finance

STA 2530H

Applied TimeSeries Analysis

STA 2535H

Life Insurance Mathematics

STA 2536H

Data Science for Risk Modelling

STA 2550H^{+}

Industrial Seminar Series

Winter Session
STA 2540H

Insurance Risk Management

STA 2546H  Data Analytics in Practice 
STA 2550H^{+}

Industrial Seminar Series

STA 2551H

Finance and Insurance Case Studies

STA 2570H

Numerical Methods for Finance and Insurance

STA 45##  [To be selected by the student with approval of the Director.] 
Summer Session
STA 2560Y

Industrial Internship

^{+} Extended course. For academic reasons, coursework is extended into session following academic session in which course is offered.
Program Length
3 sessions fulltime (typical registration sequence: F/W/S)
Time Limit
3 years fulltime
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 fulltime or parttime basis. Program requirements are the same for the fulltime and parttime 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 midB 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 fullcourse equivalents (FCEs), of which 2.0 must be chosen from the list below:

STA 2101H Methods of Applied Statistics I

STA 2201H Methods of Applied Statistics II

STA 2111H Probability Theory I

STA 2211H Probability Theory II

STA 2112H Mathematical Statistics I

STA 2212H Mathematical Statistics II


The remaining 2.0 FCEs may be selected from:

any Department of Statistical Sciences 2000level course or higher

any 1000level 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 4500level 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.

Parttime 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 fulltime (typical registration sequence: F/W/S);
6 sessions parttime
Time Limit
3 years fulltime;
6 years parttime
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 fullcourse equivalents (FCEs):

STA 2111H Probability Theory I

STA 2211H Probability Theory II

STA 2101H Methods of Applied Statistics I

STA 2201H Methods of Applied Statistics II

STA 3000Y Advanced Theory of Statistics

Comprehensive Examination Requirements

At the end of Year 1, students must attempt the following comprehensive examinations:

Probability

Theoretical Statistics

Applied Statistics
All three examinations must be passed by the end of Year 2.

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 twoyear residency requirement, whereby students must be on campus fulltime 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 (DirectEntry)
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 fullcourse equivalents (FCEs):

STA 2111H Probability Theory I

STA 2211H Probability Theory II

STA 2101H Methods of Applied Statistics I

STA 2201H Methods of Applied Statistics II

STA 3000Y 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

At the end of Year 1, students must attempt the following comprehensive examinations:

Probability

Theoretical Statistics

Applied Statistics
All three examinations must be passed by the end of Year 2.

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 threeyear residency requirement, whereby students must be on campus fulltime 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 fullcourse equivalents (FCEs):

All of:

STA 2111H Probability Theory I,

STA 2211H Probability Theory II, and

STA 2503H Applied Probability for Mathematical Finance


One of:

STA 4246H Research Topics in Mathematical Finance or

STA 2501H Mathematical Risk Theory


Either:

STA 3000Y Advanced Theory of Statistics or

STA 2101H Methods of Applied Statistics I and

STA 2201H Methods of Applied Statistics II.


Comprehensive Examination Requirements

At the end of Year 1, students must attempt the following comprehensive examinations:

Probability

Actuarial Science and Mathematical Finance

Theoretical Statistics or Applied Statistics
All three examinations must be passed by the end of Year 2.

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 threeyear residency requirement, whereby students must be on campus fulltime 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
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. Visit the department's website for courses offered in the current academic year.
STA 1001H

Applied Regression Analysis

STA 1002H

Methods of Data Analysis

STA 1003H

Sample Survey Theory and its Application

STA 1004H

Introduction to Experimental Design

STA 1007H

Statistics for Life and Social Scientists

JAS 1101H  Topics in Astrostatistics 
STA 2005H

Applied Multivariate Analysis

STA 2006H

Applied Stochastic Processes

STA 2016H

Theory and Methods for Complex Spatial Data (prerequisite: STA302H1)

STA 2080H

Fundamentals of Statistical Genetics

STA 2101H

Methods of Applied Statistics I

STA 2102H

Computational Techniques in Statistics

STA 2104H

Statistical Methods for Machine Learning and Data Mining

STA 2111H

Probability Theory I

STA 2112H

Mathematical Statistics I

STA 2201H

Methods of Applied Statistics II

STA 2202H

Time Series Analysis

STA 2211H

Probability Theory II

STA 2212H

Mathematical Statistics II

STA 2453H

Data Science Methods, Collaboration, and Communication

STA 2501H

Mathematical Risk Theory

STA 2502H

Stochastic Models in Investments

STA 2503H

Applied Probability for Mathematical Finance

STA 2505H

Credibility Theory and Simulation Methods

STA 2530H

Applied TimeSeries Analysis

STA 2535H

Life Insurance Mathematics

STA 2536H

Data Science for Risk Modelling

STA 2540H

Insurance Risk Management

STA 2546H  Data Analytics in Practice 
STA 2550H^{+}

Industrial Seminar Series

STA 2551H

Finance and Insurance Case Studies

STA 2555H

Information Visualization

STA 2560Y

Industrial Internship

STA 2570H

Numerical Methods for Finance and Insurance

STA 2600H

Teaching and Learning of Statistics in Higher Education

STA 2700H

Computational Inference and Graphical Models

STA 3000Y

Advanced Theory of Statistics

STA 3431H

Monte Carlo Methods

STA 4000H, Y

Supervised Reading Project I

STA 4001H, Y

Supervised Reading Project II

STA 4002H

Supervised Reading Project for an Advanced Special Topic

STA 4246H

Research Topics in Mathematical Finance

STA 4273H

Research Topics in Statistical Machine Learning

STA 4364H

Conditional Inference: Sample Space Analysis

STA 4372H  Foundations of Statistical Inference 
STA 4412H

Topics in Theoretical Statistics Modular Courses

Note: The following modular courses are each worth 0.25 fullcourse equivalent (FCE).
STA 4500H

Statistical Dependence: Copula Models and Beyond

STA 4501H

Functional Data Analysis and Related Topics

STA 4502H

Topics in Stochastic Processes

STA 4505H

Applied Stochastic Control: High Frequency and Algorithmic Trading

STA 4506H

Nonstationary Time Series Analysis

STA 4507H

Extreme Value Theory and Applications

STA 4508H

Topics in Likelihood Inference

STA 4509H

Insurance Risk Models I

STA 4510H

Insurance Risk Models II

STA 4511H

Statistical Issues in Number Theory

STA 4512H

Logical Foundations of Statistical Inference

STA 4513H

Statistical Models of Networks, Graphs, and Other Relational Structures

STA 4514H

Modelling and Analysis of Spatially Correlated Data

STA 4515H

Multiple Hypothesis Testing and its Applications

STA 4516H

Topics in Probabilistic Programming

STA 4517H

Foundations and Trends in Causal Inference

STA 4518H

Robust Statistical Methods (prerequisite: STA 2112H or permission)

STA 4519H  Optimal Transport: Theory and Algorithms (prerequisites: STA 2111H and STA 2211H, or permission by the instructor) 
STA 4522H

The Measurement of Statistical Evidence

STA 4523H

Bayesian Computation with Massive Data and Intractable Likelihoods

STA 4524H

Advanced Topics in Statistical Genetics

STA 4525H

Demographic Methods

STA 4526H  Stochastic Control and Applications in Finance 
STA 4527H  Random Matrix Theory and Its Applications 
STA 4528H  Dependence Modelling With Application to Risk Management 
^{+} Extended course. For academic reasons, coursework is extended into session following academic session in which course is offered.