Last Updated on August 31, 2023

Masters in Statistics Admission Requirements: A Comprehensive GuideIn an increasingly data-driven world, the demand for statisticians has reached new heights. A master’s degree in statistics not only equips individuals with the necessary skills to analyze and interpret complex data, but also opens up a wide range of career prospects. However, gaining admission into a prestigious statistics program requires meeting certain admission requirements. In this article, we will delineate the essential prerequisites for aspiring candidates.

A bachelor’s degree in statistics or a related quantitative field is typically the primary requirement for admission to a master’s program in statistics. However, many institutions also consider candidates from diverse backgrounds, such as mathematics, economics, computer science, or engineering. Having a solid foundation in mathematics, including coursework in calculus, linear algebra, probability, and mathematical statistics, is

## 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.

### 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 |

STA 45## | [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*

- STA2101H
- 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*.

- STA2111H

##### 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*.

- STA2111H
- 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.

#### 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*.

- STA2111H
- One of:
- STA4246H
*Research Topics in Mathematical Finance***or** - STA2501H
*Mathematical Risk Theory*.

- STA4246H
- Either:
- STA3000Y
*Advanced Theory of Statistics***or** - STA2101H
*Methods of Applied Statistics I***and** - STA2201H
*Methods of Applied Statistics II.*

- STA3000Y

- All of:

##### 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.