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 Astronomy and Astrophysics, the Dalla Lana School of Public Health, the Faculty of Information, Mathematics, Philosophy, Psychology, Sociology, the Rotman School of Management, and the School of the Environment. The Department of Statistical Sciences 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

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
Duvenaud, David - PhD
Escobar, Michael - BS, PhD
Evans, Michael - BSc, MSc, PhD
Feuerverger, Andrey - BSc, PhD
Fortin, Marie-Josée - MSc, PhD, CRC
Franklin, Meredith - BSc, MSc, PhD
Goldenberg, Anna - PhD, PhD
Gong, Ruobin - PhD
Gronsbell, Jessica - BA, PhD
Jaimungal, Sebastian - BSc, MSc, PhD (Chair and Graduate Chair)
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
Roy, Daniel - BS, MEng, PhD
Seco, Luis - PhD
Shi, Xiaofei - MSc, PhD
Speagle, Joshua - MA, PhD
Stafford, James - BS, MS, PhD
Strug, Lisa - BS, BA, SM, PhD
Sun, Lei - BS, PhD
Sun, Qiang - BSc, PhD
Tyrrell, Pascal - BSc, MSc, PhD
Urtasun, Raquel - PhD
Virag, Balint - BA, MA, PhD
Volgushev, Stanislav - MA, PhD (Associate Chair, Graduate Studies)
Zhou, Zhou - MSc, DPhil
Zwiernik, Piotr - MSc, MSc, PhD

Members Emeriti

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

Associate Members

Butler, Kenneth - BS, MS, PhD
Caetano, Samantha-Jo - BSc, MSc, PhD
Campbell, Kieran - PhD
Daignault, Katherine Suzanne - MSc, PhD
Gibbs, Alison - BSc, MSc, PhD
Maddison, Christopher - PhD
Moon, Michael jongho - MSc
Moon, Nathalie - BSc, MMath, PhD
Schwartz, Scott - BS, BA, 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

The Master of Financial Insurance (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.

Master of Financial Insurance

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.

Completion Requirements

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

    • Eight required half courses (4.0 FCEs).

    • STA2546H Data Analytics in Practice.

    • 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. Students must submit a project proposal to the program director and select an advisor 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

Mode of Delivery: In person
Program Length: 3 sessions full-time (typical registration sequence: FWS)
Time Limit: 3 years full-time

 


Statistical Sciences: Statistics MSc

Students in the Master of Science (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.

MSc Program; 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.

Completion 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:

  • 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.

Mode of Delivery: In person
Program Length: 3 sessions full-time (typical registration sequence: FWS); 6 sessions part-time
Time Limit: 3 years full-time; 6 years part-time

 


Statistical Sciences: Statistics PhD

Students in the Doctor of Philosophy (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).



Statistical Sciences: Statistics PhD; 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 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.

Completion Requirements

Course Requirements

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

    • (1.5 FCEs) All of:

      • STA2111H Probability Theory I,

      • STA2211H Probability Theory II, and

      • STA2503H Applied Probability for Mathematical Finance.

    • (0.5 FCE) One of:

      • STA2501H Advanced Topics in Actuarial Science or

      • STA4246H Research Topics in Mathematical Finance.

    • (1.0 FCE) One of:

      • STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II or

      • STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II or

      • 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 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.

Mode of Delivery: In person
Program Length: 4 years full-time (typical registration sequence: Continuous)
Time Limit: 6 years full-time

 


Statistical Sciences: Statistics PhD; Field: 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 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.

Completion Requirements

Course Requirements

  • During Year 1, students must successfully complete a total of 3.0 full-course equivalents (FCEs) as follows:

    • STA3000Y Advanced Theory of Statistics

    and two of the following:

    • STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II

    • STA2111H Probability Theory I and STA2211H Probability Theory II

    • STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II.

  • Courses must be chosen in consultation with the advisor and 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 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.

Mode of Delivery: In person
Program Length: 4 years full-time (typical registration sequence: Continuous)
Time Limit: 6 years full-time

 

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.

Completion Requirements

Course Requirements

  • Students must successfully complete a total of 5.0 full-course equivalents (FCEs) as follows:

    • Year 1: complete 3.0 FCEs:

      • STA3000Y Advanced Theory of Statistics

        and two of the following:

      • STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II

      • STA2111H Probability Theory I and STA2211H Probability Theory II

      • STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II.

      • Courses must be chosen in consultation with the advisor and approved by the Associate Chair of Graduate Studies.

    • 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.

Mode of Delivery: In person
Program Length: 5 years full-time (typical registration sequence: Continuous)
Time Limit: 7 years full-time

 


Statistical Sciences: Statistics PhD; Field: Statistical Theory and Applications

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 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.

Completion Requirements

Course Requirements

  • During Year 1, students must successfully complete a total of 3.0 full-course equivalents (FCEs) as follows:

    • STA3000Y Advanced Theory of Statistics

    and two of the following:

    • STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II

    • STA2111H Probability Theory I and STA2211H Probability Theory II

    • STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II.

  • Courses must be chosen in consultation with the advisor and 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 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.

Mode of Delivery: In person
Program Length: 4 years full-time (typical registration sequence: Continuous)
Time Limit: 6 years full-time

 

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.

Completion Requirements

Course Requirements

  • Students must successfully complete a total of 5.0 full-course equivalents (FCEs) as follows:

    • Year 1: complete 3.0 FCEs:

      • STA3000Y Advanced Theory of Statistics

        and two of the following:

      • STA2101H Methods of Applied Statistics I and STA2201H Methods of Applied Statistics II

      • STA2111H Probability Theory I and STA2211H Probability Theory II

      • STA2311H Advanced Computational Methods for Statistics I and STA2312H Advanced Computational Methods for Statistics II.

      • Courses must be chosen in consultation with the advisor and approved by the Associate Chair of Graduate Studies.

    • 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.

Mode of Delivery: In person
Program Length: 5 years full-time (typical registration sequence: Continuous)
Time Limit: 7 years full-time

 


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.

Course CodeCourse Title
Statistics for Life and Social Scientists
STA1008HApplications of Statistics
JAS1101HTopics in Astrostatistics
Applied Multivariate Analysis
Applied Stochastic Processes
Theory and Methods for Complex Spatial Data
STA2047HStochastic Calculus
STA2051HTopics in Numerical Methods in Data Science
STA2052HStatistics, Ethics, and Law
STA2053HSpecial Topics in Applied Statistics
Fundamentals of Statistical Genetics
Methods of Applied Statistics I
Computational Techniques in Statistics
Statistical Methods for Machine Learning and Data Mining
Probability Theory I
Mathematical Statistics I
STA2162HStatistical Inference I
STA2163HOnline Learning and Sequential Decision Theory
Methods of Applied Statistics II
Time Series Analysis
STA2209HLifetime Date Modelling and Analysis
Probability Theory II
Mathematical Statistics II
STA2311HAdvanced Computational Methods for Statistics I
STA2312HAdvanced Computational Methods for Statistics II
Data Science Methods, Collaborations, and Communication
STA2500HLoss Models
Advanced Topics in Actuarial Science
Stochastic Models in Investments
Applied Probability for Mathematical Finance
Credibility Theory and Simulation Methods
Applied Time-Series Analysis
Life Insurance Mathematics
Data Science for Risk Modelling
Insurance Risk Management
STA2546HData Analytics in Practice
Industrial Seminar Series
Finance and Insurance Case Studies
Information Visualization
Industrial Internship
Numerical Methods for Finance and Insurance
Teaching and Learning of Statistics in Higher Education
Computational Inference and Graphical Models
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
Research Topics in Statistical Machine Learning
STA4372HFoundations of Statistical Inference

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

Course CodeCourse Title
Statistical Dependence: Copula Models and Beyond
Functional Data Analysis and Related Topics
Topics in Stochastic Processes
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
Topics in Insurance Risk Modelling II
Logical Foundations of Statistical Inference
Modelling and Analysis of Spatially Correlated Data
Multiple Hypothesis Testing and its Applications
Topics in Probabilistic Programming
Foundations and Trends in Causal Inference
Robust Statistical Methods
STA4519HOptimal Transport: Theory and Algorithms
The Measurement of Statistical Evidence
Bayesian Computation with Massive Data and Intractable Likelihoods
Advanced Topics in Statistical Genetics
Demographic Methods
STA4526HStochastic Control and Applications in Finance
STA4527HRandom Matrix Theory and Its Applications
STA4528HDependence Modelling With Application to Risk Management
STA4529HApplications of Nonstandard Analysis to Statistics and Probability Theory
STA4530HDerivatives for Institutional Investing
STA4531HInformation Geometry