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Published 2016-17 SGS Calendar for editing
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Statistical Sciences: Introduction
Faculty Affiliation
Arts and Science
Degree Programs
Financial Insurance
MFI
Statistics
MSc
PhD
Fields:
Statistical Theory and Applications
Probability
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, 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.
The Department of Statistical Sciences offers the following degree programs:
The Master of Financial Insurance (MFI) is a full-time professional program based on three
pillars: statistical methods, 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.
Students in the Master of Science program can conduct research in the fields of (a) Statistical
Theory and Applications or (b) Probability.
Students in the Doctor of Philosophy program can conduct research in the fields of (a)
Statistical Theory and Applications or (b) Probability or (c) Actuarial Science and
Mathematical Finance.
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: [email protected]
Telephone: 416-978-5136
Fax: 416-978-5133
Department of Statistical Sciences
Sidney Smith Hall
University of Toronto
Room 6018, 100 St. George Street
Toronto, Ontario M5S 3G3
Canada
MSc and PhD Programs
Web: www.utstat.utoronto.ca
Email: [email protected]
Telephone: (416) 978-5136
Fax: (416) 978-5133
Department of Statistical Sciences
University of Toronto
Sidney Smith Hall
Room 6022, 100 St. George Street
Toronto, Ontario M5S 3G3
Canada
Statistical Sciences: Financial Insurance MFI
Master of Financial Insurance
Minimum Admission Requirements
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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.
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
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Students must successfully complete 5.5 full-course equivalents (FCEs) as follows:
o Nine required half courses (4.5 FCEs).
o STA 2560Y Industrial Internship, a 3.5-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
MMF 2021H
STA 2503H
STA 2530H
STA 2535H
STA 2550H+
Numerical Methods for Finance
Applied Probability for Mathematical Finance
Applied Time-Series Analysis
Life Insurance Mathematics
Financial Insurance Seminar Series (Credit/No Credit)
Winter Session
ECO 2506H
STA 2540H
STA 2551H
STA 2536H
STA 2550H+
Economics of Risk Management
Insurance Risk Management
Financial Insurance Case Studies
Non-life Insurance Mathematics
Financial Insurance Seminar Series (Credit/No Credit)
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 full-time (typical registration sequence: F/W/S)
Time Limit
3 years full-time
Statistical Sciences: Statistics MSc
Master of Science
Fields: Statistical Theory and Applications Probability
Minimum Admission Requirements
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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:
o 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.
o An average grade equivalent to at least a University of Toronto mid-B in the
final year or over senior courses.
o Three letters of reference.
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 specified in the General
Regulations of the School of Graduate Studies.
Program Requirements
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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.
Full-Time Program
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Students must complete a total of 4.0 full-course equivalents (FCEs), of which 2.0 must
be chosen from the list below:
o STA 2101H Methods of Applied Statistics I.
o STA 2201H Methods of Applied Statistics II
o STA 2111H Probability Theory I
o STA 2211H Probability Theory II
o STA 2112H Mathematical Statistics I
o STA 2212H Mathematical Statistics II.
The remaining 2.0 FCEs may be selected from:
o any Department of Statistical Sciences 2000-level course or higher
o any 1000-level course or higher in another graduate unit at the University of
Toronto with sufficient statistical, computational, probabilistic, or mathematical
content
o one 0.5 FCE as a reading course
o one 0.5 FCE as a research project
o
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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 Program
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Students must satisfy the program requirements outlined for the full-time MSc.
Students are limited to taking 1.0 full-course equivalent (FCE) during each session. In
exceptional cases, the Associate Chair for Graduate Studies may approve 1.5 FCE in a
given session. Both the Statistical Theory and Applications field and the Probability
field are open to part-time students.
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
Minimum Admission Requirements
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Admission to the PhD program is competitive, and applicants are admitted under the
General Regulations of the School of Graduate Studies.
Students may be accepted through one of two routes: a master's degree or by direct
entry through a bachelor's degree. Successful applicants present either:
1. 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.
2. A bachelor's degree in statistics from a recognized university with at least an Aaverage. 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.
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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 using one of the official methods specified in the General
Regulations of the School of Graduate Studies.
Program Requirements
Fields:
Statistical Theory and Applications
Probability
Course Requirements:
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During the first year of study, students are required to complete the following 3.0 fullcourse equivalents (FCEs):
o STA 2111H Probability Theory I
o STA 2211H Probability Theory II
o STA 2101H Methods of Applied Statistics I
o STA 2201H Methods of Applied Statistics II
o STA 3000Y Advanced Theory of Statistics.
Comprehensive Examination Requirements:
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At the end of the first year, students must attempt the following comprehensive
examinations:
o Probability
o Theoretical Statistics
o Applied Statistics.
All three examinations must be passed by the end of the second year.
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.
Program Requirements
Field: Actuarial Science and Mathematical Finance
Course Requirements:
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During the first year of study, students are required to complete the following 3.0 fullcourse equivalents (FCEs):
1. All of:
 STA 2111H Probability Theory I,
 STA 2211H Probability Theory II, and
 STA 2503H Applied Probability for Mathematical Finance
2. One of:
 STA 4246H Research Topics in Mathematical Finance or
 STA 2501H Mathematical Risk Theory
3. 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:
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At the end of the first year, 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 the second year.
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.
Direct-Entry PhD Program Requirements
The program requirements are identical to the regular PhD program in the respective fields
with the exception that 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.
Residency Requirements:
Students must also satisfy a three-year residency requirement.
Program Length
4 years full-time; 5 years direct-entry
Time Limit
6 years full-time; 7 years direct-entry
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
STA 1002H
STA 1003H
STA 1007H
STA 1008H
STA 2004H
STA 2005H
STA 2006H
STA 2047H
STA 2080H
STA 2100H
STA 2101H
STA 2102H
STA 2104H
STA 2105H
Applied Regression Analysis
Methods of Data Analysis
Sample Survey Theory and its Application
Statistics for Life and Social Scientists
Applications of Statistics
Design of Experiments
Applied Multivariate Analysis
Applied Stochastic Processes
Stochastic Calculus
Fundamentals of Statistical Genetics
Mathematical Methods for Statistics
Methods of Applied Statistics I
Computational Techniques in Statistics
Statistical Methods for Machine Learning and Data Mining
Nonparametric Methods of Statistics
STA 2111H
STA 2112H
STA 2162H
STA 2201H
STA 2202H
STA 2209H
STA 2211H
STA 2212H
STA 2342H
STA 2453H
STA 2500H
STA 2501H
STA 2502H
STA 2503H
STA 2505H
STA 2542H
STA 2530H
STA 2535H
STA 2536H
STA 2540H
STA 2550H+
STA 2551H
STA 2560Y
STA 3000Y
STA 3431H
STA 4000H, Y
STA 4001H, Y
STA 4002H
STA 4246H
STA 4247H
STA 4273H
STA 4315H
STA 4364H
STA 4412H
Probability Theory I
Mathematical Statistics I
Statistical Inference I
Methods of Applied Statistics II
Time Series Analysis
Lifetime Date Modelling and Analysis
Probability Theory II
Mathematical Statistics II
Multivariate Analysis I
Statistical Consulting
Loss Models
Mathematical Risk Theory
Stochastic Models in Investments
Applied Probability for Mathematical Finance
Credibility Theory and Simulation Methods
Linear Models
Applied Time-Series Analysis
Life Insurance Mathematics
Non-life Insurance Mathematics
Insurance Risk Management
Financial Insurance Seminar Series (Credit/No Credit)
Financial Insurance Case Studies
Industrial Internship
Advanced Theory of Statistics
Monte Carlo Methods
Supervised Reading Project I
Supervised Reading Project II
Supervised Reading Project for an Advanced Special Topic
Research Topics in Mathematical Finance
Point Processes, Noise, and Stochastic Analysis
Research Topics in Statistical Machine Learning
Computational Methods in Statistical Genetics
Conditional Inference: Sample Space Analysis
Topics in Theoretical Statistics Modular Courses
Note: The following modular courses are each worth 0.25 full-course equivalents (FCEs).
STA 4500H
STA 4501H
STA 4502H
STA 4503H
STA 4504H
Statistical Dependence: Copula Models and Beyond
Functional Data Analysis and Related Topics
Monte Carlo Estimation
Advanced Monte Carlo Methods and Applications
An Introduction to Bootstrap Methods
STA 4505H
STA 4506H
STA 4507H
STA 4508H
STA 4509H
STA 4510H
STA 4511H
STA 4512H
STA 4513H
STA 4514H
STA 4516H
+
Applied Stochastic Control: High Frequency and Algorithmic Trading
Non-stationary Time Series Analysis
Extreme Value Theory and Applications
Topics in Likelihood Inference
Insurance Risk Models I
Insurance Risk Models II
Statistical Issues in Number Theory
Logical Foundations of Statistical Inference
Statistical Models of Networks, Graphs, and Other Relational Structures
Modelling and Analysis of Spatially Correlated Data
Topics in Probabilistic Programming
Extended course. For academic reasons, coursework is extended into session following
academic session in which course is offered.