Statistics


Faculty List

  • K. Butler, B.Sc. (Birmingham), M.Sc., Ph.D. (Simon Fraser), Assistant Professor, Teaching Stream
  • S. Damouras, B.Sc. (Athens Univ. of Econ. and Bus.), M.Sc. (Warwick), Ph.D. (Carnegie Mellon), Associate Professor, Teaching Stream
  • M. Evans, B.Sc. (Western Ontario), M.Sc., Ph.D. (Toronto), Professor
  • S. Kang, B.Sc., M.Sc. (Chonnam National University, South Korea), M.Sc., Ph.D. (Toronto), Associate Professor, Teaching Stream
  • D. Roy, B.Sc., M.Eng., Ph.D. (MIT), Associate Professor
  • M. Samarakoon, B.Sc. (Colombo), M.Sc. (Alberta), Ph.D. (Toronto), Associate Professor, Teaching Stream
  • S. Shams, B.Sc. (Dhaka), M.A. (York), M.Sc., Ph.D. (Toronto), Assistant Professor, Teaching Stream
  • Q. Sun, B.Sc. (Science & Technology, China), Ph.D. (North Carolina), Associate Professor
  • B. Virag, B.A. (Harvard), M.A., Ph.D. (Berkeley), Professor
  • L. Wang, B.Sc. (Peking), Ph.D. (Washington), Assistant Professor
  • L. Wong, B.Sc., M.Phil. (Hong Kong), Ph.D. (Washington), Assistant Professor


Associate Chair: D. Roy   Email: daniel.roy@utoronto.ca

Probability and Statistics have developed over a period of several hundred years as attempts to quantify uncertainty. With its origins in modelling games of chance, probability theory has become a sophisticated mathematical discipline with applications in such fields as demography, genetics and physics.

Statistics is concerned with the proper collection and analysis of data, both to reduce uncertainty and to provide for its assessment via probability. Applications range from pre-election polling to the design and analysis of experiments to determine the relative efficacies of different vaccines.

STAB22H3 and STAB27H3 serve as a non-technical introduction to statistics. These courses are designed for students from disciplines where statistical methods are applied. STAB52H3 is a mathematical treatment of probability. STAB57H3 is an introduction to the methods and theory of statistical inference. The C-level courses build on the introductory material to provide a deeper understanding of the statistical methodology and of its practical implementation.

Admission to Statistics Programs

Beginning in 2018-19 there are admissions criteria for the Major/Major (Co-op) Program in Statistics. Details and information on how to apply for admission to these programs are found in the program descriptions below.

Combining Statistics and Economics Programs

Students who wish to combine studies in statistics and economics should consult the Economics for Management Studies section of the Calendar for information on the economics programs and restrictions on the order in which courses must be taken.

Double Degrees: BBA/BSc

The Department of Computer and Mathematical Sciences, in partnership with the Department of Management, offers the following Double Degree programs:

  • Double Degree: BBA, Specialist program in Management and Finance/Honours BSc, Specialist program in Statistics, Quantitative Finance Stream
  • Double Degree: BBA, Specialist (Co-op) program in Management and Finance/Honours BSc, Specialist (Co-op) program in Statistics, Quantitative Finance Stream

The Double Degree programs create an accelerated pathway for students who would otherwise have to complete two separate Specialist programs. They explicitly focus on finance and quantitative methods, providing students with a thorough education in both the business and the quantitative aspects of the financial industry. The Double Degree Programs take advantage of existing synergies to allow students to complete both undergraduate programs and degrees within five years without compromising their learning experience. Students will complete a total of 25.0 credits and, for those enrolled in the Double Degree (Specialist Co-op programs), students must also complete three mandatory Co-op work terms. For more information, including Admission and Program requirements, see the Double Degree Programs section of the Calendar.

Program Combination Restrictions in Statistics

The Specialist/Specialist Co-op, Major/Major Co-op, Minor in Statistics, and Minor in Applied Statistics cannot be combined.

Experiential Learning and Outreach

For a community-based experiential learning opportunity in your academic field of interest, consider the course CTLB03H3, which can be found in the Teaching and Learning section of the Calendar.

Double Degree Programs

DOUBLE DEGREE: BBA, SPECIALIST PROGRAM IN MANAGEMENT AND FINANCE / HONOURS BSc, SPECIALIST PROGRAM IN STATISTICS, QUANTITATIVE FINANCE STREAM

Academic Directors:
S. Ahmed Email: mgmtss.utsc@utoronto.ca (BBA)
S. Damouras Email: sotirios.damouras@utoronto.ca (BSc)

This Double Degree program combines the Specialist Program in Management and Finance and the Specialist Program in Statistics, Quantitative Finance stream. Students completing the Double Degree program will qualify to graduate with two-degree designations – the Bachelor of Business Administration (BBA) and the Honours Bachelor of Science (BSc), assuming all other degree criteria are met.

Enrolment Requirements

Enrolment in this Double Degree program is limited.

1. Students applying directly from high school are admitted on the basis of academic performance. They must have completed Grade 12 English, Grade 12 Advanced Functions, and Grade 12 Calculus & Vectors. Applicants must also submit a Supplementary Application Form.

Course Guidelines for Students Admitted to the Double Degree Program Directly from High School
Students must complete the following courses in their first year of study: MGEA02H3, MGEA06H3, MATA22H3, MATA31H3, MATA37H3, MATA67H3/CSCA67H3, MGAB01H3, MGAB02H3, MGHA12H3, MGMA01H3 and MGTA38H3.

2. Students already pursuing a BBA program and degree may apply to enter the Double Degree program. The application can be made before the end of the Winter semester and/or before the end of the Summer semester. Application for admission will be considered only for the round during which the student has made the Subject POSt request. Students considering switching to the Double Degree program should consult with the program supervisors as soon as possible.

The minimum Cumulative Grade Point Average (CGPA) for admission is calculated for each application period, and is based on University of Toronto courses only. Decisions are made when all grades have been received.

Students must have completed at least 5.0 credits (none of which can be designated as CR/NCR), including: MGEA02H3, MGEA06H3, MGAB01H3, MGAB02H3, MGTA38H3, MATA22H3, MGHA12H3/(MGHB12H3), MATA31H3, MATA37H3, and MATA67H3/CSCA67H3.

Students who have taken MATA34H3 or [[MATA32H3 or MATA30H3] and [MATA33H3 or MATA36H3]] instead of [MATA31H3 and MATA37H3] can still apply to the Double Degree program if they are taking or plan to take MATA37H3 at the time of application and could receive admission conditional on their grade in MATA37H3 being above a threshold to be specified each year.

Notes:

  1. Students MUST complete the pre-requisite of MATA67H3/CSCA67H3 in order to take MATA37H3.
  2. MATA34H3 is not a substitute for MATA31H3. Students who have completed MATA34H3 will be required to take MATA31H3 as an Extra (EXT) course before taking MATA37H3.

Students may apply until they have completed up to 10.0 credits. Students who have completed more than 10.0 credits will not be admitted to the Double Degree program. For those who apply with more than 5.0 credits, their CGPA at the time of application will be calculated with more weight assigned to the required courses listed under the 5.0 credits. 

CGPA Requirement to Remain in the Double Degree (Specialist Programs)

In order to remain in the Double Degree, students must maintain a CGPA of 2.0 or higher after having attempted at least 4.0 credits. Students whose CGPA falls below 2.1 (but not below 2.0) will have the opportunity to move to either the non Co-op BBA Specialist Program in Management and Finance, or the non Co-op BSc Specialist Program in Statistics, Quantitative Finance stream. If they choose to stay in the Double Degree program and their CGPA falls below 2.0, they will be removed from the Double Degree program. Students removed from the program for this reason may request re-instatement if they complete at least 2.0 credits (none of which can be designated as CR/NCR) in the following session and raise their CGPA to at least 2.0. This opportunity will be provided only once.

Program Requirements
The Double Degree program requires the completion of 25.0 credits. 21.5 credits are core program requirements as listed below, and 3.5 further credits are required to complete degree requirements. 

NOTE: Students who have taken STAB52H3 and STAB57H3 and then transfer to any other BBA program must also take MGEB12H3 to fulfill the program requirements.

1. Communications requirement (0.5 credit)
MGTA38H3 Management Communications

2. Management requirements (5.0 credits)
MGAB01H3 Introductory Financial Accounting I
MGAB02H3 Introductory Financial Accounting II
MGAB03H3 Introductory Management Accounting
MGHB02H3 Managing People and Groups in Organizations
MGHA12H3/(MGHB12H3) Human Resource Management
MGHC02H3 Management Skills
MGMA01H3 Principles of Marketing
MGMB01H3 Marketing Management
MGOC10H3 Analysis for Decision-Making
MGOC20H3 Operations Management: A Mathematical Approach

3. Science requirements (9.0 credits)
CSCA08H3 Introduction to Computer Science I
MATA67H3/CSCA67H3 Discrete Mathematics
CSCC37H3 Introduction to Numerical Algorithms for Computational Mathematics
MATA22H3 Linear Algebra I for Mathematical Sciences
MATA31H3 Calculus I for Mathematical Sciences
MATA37H3 Calculus II for Mathematical Sciences
MATB24H3 Linear Algebra II
MATB41H3 Techniques of the Calculus of Several Variables I
MATB42H3 Techniques of the Calculus of Several Variables II
MATB44H3 Differential Equations I
MATB61H3 Linear Programming and Optimization
MATC46H3 Differential Equations II
STAB52H3 An Introduction to Probability
STAB57H3 An Introduction to Statistics
STAC62H3 Probability and Stochastic Processes I
STAC67H3 Regression Analysis
STAD37H3 Multivariate Analysis
STAD57H3 Time Series Analysis

4. Economics requirements (2.0 credits)
MGEA02H3 Introduction to Microeconomics: A Mathematical Approach
MGEA06H3 Introduction to Macroeconomics: A Mathematical Approach
MGEB02H3 Price Theory: A Mathematical Approach
MGEB06H3 Macroeconomic Theory and Policy: A Mathematical Approach

5. Finance requirements (3.0 credits)
MGFB10H3 Principles of Finance
MGFC10H3 Intermediate Finance
[MGFC30H3 Introduction to Derivatives Markets or STAB41H3 Financial Derivatives]
MGFC35H3/(MGFD10H3) Investments
STAC70H3 Statistics and Finance I
STAD70H3 Statistics and Finance II

6. At least four courses (2.0 credits) from:
MGEC71H3 Money and Banking
MGFC20H3 Personal Financial Management
MGFC45H3 Portfolio Management: Theory & Practice
MGFC50H3 International Financial Management
MGFC60H3 Financial Statement Analysis & Security Valuation
MGFD15H3 Special Topics in Finance: Private Equity
MGFD25H3 Financial Technologies and Applications (FinTech) 
MGFD30H3 Risk Management
MGFD40H3 Investor Psychology and Behavioural Finance
MGFD50H3 Mergers and Acquisitions: Theory and Practice
MGFD60H3 Financial Modeling and Trading Strategies
MGFD70H3 Advanced Financial Management

NOTE: In selecting options and electives, students should refer to the guidelines for program breadth and depth found in section 6A.2 (Degree Requirements) of the Calendar.

DOUBLE DEGREE: BBA, SPECIALIST (CO-OPERATIVE) PROGRAM IN MANAGEMENT AND FINANCE / HONOURS BSc, SPECIALIST (CO-OPERATIVE) PROGRAM IN STATISTICS, QUANTITATIVE FINANCE STREAM

Assistant Director: P. Brown (416-287-7421)  Email: mgmtcoop.utsc@utoronto.ca
Management Co-op Academic Director: S. Ahmed  E-mail: mgmtss.utsc@utoronto.ca
Double Degree in Quantitative Finance and Statistics Co-op Supervisor of Studies: S. Damouras  E mail: sotirios.damouras@utoronto.ca 

Academic Directors:
S. Ahmed Email: mgmtss.utsc@utoronto.ca (BBA)
S. Damouras Email: sotirios.damouras@utoronto.ca  (BSc)

Program Director: C. Arsenault E-mail: mgmtcoop.utsc@utoronto.ca

The Double Degree program combines the Specialist (Co-operative) Program in Management and Finance and the Specialist (Co-operative) Program in Statistics, Quantitative Finance stream. Students completing the Double Degree program will qualify to graduate with two degree designations – the Bachelor of Business Administration (BBA) and the Honours Bachelor of Science (BSc), assuming all other degree criteria are met.

The Double Degree program is a Work Integrated Learning (WIL) program that combines academic studies with paid work terms in public and private enterprises. Depending on their needs and abilities, students work in areas such as finance, insurance, data analytics, accounting, consulting, business intelligence, marketing, policy, strategic planning and entrepreneurship. The Double Degree program will equip students with a comprehensive understanding of financial markets, and develop the business and quantitative skills required to function in them.

The Double Degree program operates on a trimester schedule, featuring three terms (Fall, Winter and Summer) in each Calendar year. Students work or study in all three terms for five years, or until graduation requirements are met. It requires 11 four-month terms of study and 3 four-month work terms.

Enrolment Requirements

Enrolment in the Double Degree program is limited.

1. Students applying directly from high school are admitted on the basis of academic performance. They must have completed Grade 12 English, Grade 12 Advanced Functions, and Grade 12 Calculus & Vectors. Applicants must also submit a Supplementary Application Form.

Course Guidelines for Students Admitted to Double Degree Program Directly from High School:

Students must complete the following courses in their first year of study: MGEA02H3, MGEA06H3, MATA22H3, MATA31H3, MATA37H3, MATA67H3/CSCA67H3, MGAB01H3, MGAB02H3, MGHA12H3, MGMA01H3 and MGTA38H3.

2. Students already pursuing a BBA program and degree may apply to enter this Double Degree program. The application can be made before the end of the Winter semester and/or before the end of the Summer semester. Application for admission will be considered only for the round during which the student has made the Subject POSt request. Students considering switching to the Double Degree program should consult with the program supervisors as soon as possible.

The minimum Cumulative Grade Point Average (CGPA) for Program admission is calculated for each application period, and is based on University of Toronto courses only. Decisions are made when all grades have been received.

Students must have completed at least 5.0 credits (none of which can be designated as CR/NCR), including: MGEA02H3, MGEA06H3, MGAB01H3, MGAB02H3, MGHA12H3, MGTA38H3, MATA22H3, MATA31H3, MATA37H3, and MATA67H3/CSCA67H3.

Students who have taken MATA34H3 or [[MATA32H3 or MATA30H3] and [MATA33H3 or MATA36H3]] instead of [MATA31H3 and MATA37H3] can still apply to the Double Degree program if they are taking or plan to take MATA37H3 at the time of application and could receive admission conditional on their grade in MATA37H3 being above a threshold to be specified each year.

Notes:

  1. Students MUST complete the pre-requisite of MATA67H3/CSCA67H3 in order to take MATA37H3.
  2. MATA34H3 is not a substitute for MATA31H3. Students who have completed MATA34H3 will be required to take MATA31H3 as an Extra (EXT) course before taking MATA37H3. 

Students may apply until they have completed up to 10.0 credits. Students who have completed more than 10.0 credits will not be able to apply to the Double Degree Program. For those who apply with more than 5.0 credits, their CGPA at the time of application will be calculated with more weight assigned to the required courses listed under the 5.0 credits.

Applicants must submit a resume and covering letter to the Management Co-op Office during the limited Subject POSt request period outlined on the Office of the Registrar website.  For information on what to include in your resume and covering letter, visit the Management Co-op website. An interview may also be required.

CGPA Requirement to Remain in the Double Degree Co-op Program

Students must maintain a CGPA of 2.5 or higher. Students whose CGPA falls below 2.5 will be placed on probation. Students on probation will be reinstated to the Double Degree program if they complete at least 2.0 credits (none of which can be designated as CR/NCR) in the following session and raise their CGPA to at least 2.5. Students who cannot get out of probation in two consecutive sessions, or whose CGPA falls below 2.3, will be removed from the Double Degree Co-op Program. Students removed from the Double Degree (Specialist Co-op Programs) can pursue the Double Degree (Specialist Programs), or one of its non Co-op constituent programs (i.e., the BBA Specialist Program in Management and Finance, or the BSc Specialist Program in Statistics, Quantitative Finance stream).

Program Requirements
The Double Degree program requires the completion of 25.0 credits. 21.5 credits are core program requirements as listed below, and 3.5 further credits are required to complete degree requirements. 

NOTE: Students who have taken STAB53H3 and STAB57H3 and then transfer to any other BBA program must also take MGEB12H3 to fulfill the program requirements. 

1. Communications requirement (0.5 credit)
MGTA38H3 Management Communications

2. Management requirements (5.0 credits)
MGAB01H3 Introductory Financial Accounting I
MGAB02H3 Introductory Financial Accounting II
MGAB03H3 Introductory Management Accounting
[MGHB02H3 Managing People and Groups in Organizations
MGHA12H3 /(MGHB12H3) Human Resource Management
MGHC02H3 Management Skills
MGMA01H3 Principles of Marketing
MGMB01H3 Marketing Management
MGOC10H3 Analysis for Decision-Making
MGOC20H3 Operations Management: A Mathematical Approach

3. Science requirements (9.0 credits)
CSCA08H3 Introduction to Computer Science I
MATA67H3/CSCA67H3 Discrete Mathematics
CSCC37H3 Introduction to Numerical Algorithms for Computational Mathematics
MATA22H3 Linear Algebra I for Mathematical Sciences
MATA31H3 Calculus I for Mathematical Sciences
MATA37H3 Calculus II for Mathematical Science
MATB24H3 Linear Algebra II
MATB41H3 Techniques of the Calculus of Several Variables I
MATB42H3 Techniques of the Calculus of Several Variables II
MATB44H3 Differential Equations I
MATB61H3 Linear Programming and Optimization
MATC46H3 Differential Equations II
STAB52H3 An Introduction to Probability
STAB57H3 An Introduction to Statistics
STAC62H3 Probability and Stochastic Processes I
STAC67H3 Regression Analysis
STAD37H3 Multivariate Analysis
STAD57H3 Time Series Analysis

4. Economics requirements (2.0 credits)
MGEA02H3 Introduction to Microeconomics: A Mathematical Approach
MGEA06H3 Introduction to Macroeconomics: A Mathematical Approach
MGEB02H3 Price Theory: A Mathematical Approach
MGEB06H3 Macroeconomic Theory and Policy: A Mathematical Approach

5. Finance requirements (3.0 credits)
MGFB10H3 Principles of Finance
MGFC10H3 Intermediate Finance
[MGFC30H3 Introduction to Derivatives Markets or STAB41H3 Financial Derivatives]
MGFC35H3/(MGFD10H3) Investments
STAC70H3 Statistics and Finance I
STAD70H3 Statistics and Finance II

6. At least four courses (2.0 credits) from:
MGEC71H3 Money and Banking
MGFC20H3 Personal Financial Management
MGFC45H3 Portfolio Management: Theory & Practice 
MGFC50H3 International Financial Management
MGFC60H3 Financial Statement Analysis & Security Valuation
MGFD15H3 Special Topics in Finance: Private Equity
MGFD25H3 Financial Technologies and Applications (FinTech) 
MGFD30H3 Risk Management
MGFD40H3 Investor Psychology and Behavioural Finance
MGFD50H3 Mergers and Acquisitions: Theory and Practice
MGFD60H3 Financial Modeling and Trading Strategies
MGFD70H3 Advanced Financial Management

NOTE: In selecting options and electives, students should refer to the guidelines for program breadth and depth found in section 6A.2 (Degree Requirements) of the Calendar.


Co-op Work Term Requirements

All Double Degree program Co-op students must take MGTA38H3 prior to commencement of their first work term. Students are advised to consult regularly with the Academic Supervisors, or the Program Director, if they have questions regarding course selection and scheduling. It is however the students' individual responsibility to ensure that they have completed the correct courses to make them eligible for each work term and that they have correctly completed program and degree requirements for graduation.

Students who apply after the first year and are successful in receiving a June offer will be expected to complete a Co-op Advancing Your Career Exploration (AYCE) course beginning in the third week of June, and continuing throughout the summer.

To compete for a work term a student must maintain a 2.5 CGPA, and must have completed:

  1. For the first work term:
    1. 7.0 credits, including: MGEA02H3, MGEA06H3, MGAB01H3, MGAB02H3, MGHA12H3/(MGHB12H3), MGMA01H3, MGTA38H3, MATA22H3, MATA31H3 and MATA37H3.
    2. The Management Co-op Advancing Your Career Exploration Courses (AYCE): [COPB11H3 and COPB12H3] or COPB10Y3
  2. For the second work term: 9.0 credits.
  3. For the third work term: 11.0 credits.

For information on fees, status in Co-op programs, and certification of completion of Co-op programs, see Section 6B.5 of this Calendar.

 

Statistics Programs

SPECIALIST PROGRAM IN STATISTICS (SCIENCE)

Supervisor of Studies: S. Damouras Email: sotirios.damouras@utoronto.ca (416-287-7269)

Program Objectives
This program provides training in the discipline of Statistics. Students are given a thorough grounding in the theory underlying statistical reasoning and learn the methodologies associated with current applications. A full set of courses on the theory and methodology of the discipline represents the core of the program. In addition, students select one of three streams, each of which provides immediately useful, job-related skills. The program also prepares students for further study in Statistics and related fields.

The Quantitative Finance Stream focuses on teaching the computational, mathematical and statistical techniques associated with modern-day finance. Students acquire a thorough understanding of the mathematical models that underlie financial modeling and the ability to implement these models in practical settings. This stream prepares students to work as quantitative analysts in the financial industry, and for further study in Quantitative Finance.

The Statistical Machine Learning and Data Science Stream focuses on applications of statistical theory and concepts to the discovery (or “learning”) of patterns in data. This field is a recent development in statistics with wide applications in science and technology including computer vision, image understanding, natural language processing, medical diagnosis, and stock market analysis. This stream prepares students for direct employment in industry and government, and further study in Statistical Machine Learning.

The Statistical Science Stream is concerned with giving students a sound grounding in statistical methodology and theory. Students acquire expertise in the proper collection of data, the methods used to analyze data to answer scientific questions of interest, and the theory that underlies these activities. The program provides preparation for employment as a statistician or for further graduate studies in statistics.

Enrolment Requirements

Enrolment in the Specialist in Statistics (all streams) is limited. Students may apply to enter the program after completing 4.0 credits, and must meet the requirements described below:

1. Students already admitted to the UTSC Year 1 Statistics admissions category:

Required Courses:

Students must have passed the following CSC and MAT courses:

a. All streams: CSCA08H3, [CSCA67H3 or MATA67H3], MATA22H3, MATA31H3, and MATA37H3.
b. Machine Learning and Data Science stream only: CSCA48H3

Required Grades:

There are a limited number of available spaces in each stream of the Specialist in Statistics. Students that meet all of the following requirements will be eligible to be considered for one of the spaces in a Statistics Specialist POSt; admission will be based on academic performance in the required A-level courses, identified above. Students who meet all of the following requirements but are not admitted to the Specialist will be admitted to the Major in Statistics:
a. All streams: a cumulative grade point average (CGPA) of at least 2.5 over the following courses: CSCA08H3, CSC/MATA67H3, MATA22H3, MATA31H3, and MATA37H3; and
b. For the Machine Learning and Data Science stream only: a final grade of at least B in CSCA48H3.

2. Students admitted to other UTSC Year 1 admissions categories:

Students that have been admitted to other CMS admissions categories (Computer Science or Mathematics) or any other UTSC Year 1 admissions categories are eligible to apply for a Statistics Specialist POSt. Admission will be based on academic performance in the required A-level courses, identified above. The requirements change each year depending on available spaces and the pool of eligible applicants, and students are cautioned that there is no guarantee of admission; as such, students are strongly advised to plan to enrol in backup programs.

For more information about the admission requirements, please visit the following CMS webpage.

Program Requirements
To complete the program, a student must meet the course requirements described below.

The first-year requirements of the three streams are almost identical, except that the Quantitative Finance stream requires MGEA02H3 while the Statistical Machine Learning and Data Science stream requires CSCA48H3, and the Statistical Science stream requires STAA57H3; these courses need not be taken in the first year.

Note: There are courses on the St. George campus that can be taken to satisfy some of the requirements of the program. STAB52H3, STAB57H3, STAC62H3 and STAC67H3, however, must be taken at the University of Toronto Scarborough; no substitutes are permitted without permission of the program supervisor.

Core (7.5 credits)

1. Writing Requirement (0.5 credit) (*)
0.5 credit from the following: ANTA01H3, ANTA02H3, CTLA01H3, ENGA10H3, ENGA11H3, ENGB06H3, ENGB07H3, ENGB08H3, ENGB09H3, ENGB17H3, ENGB19H3, ENGB50H3, GGRA02H3, GGRA03H3, GGRB05H3, ACMA01H3, LINA01H3, PHLA10H3, PHLA11H3, WSTA01H3.
(*) It is recommended that this requirement be satisfied by the end of the second year.

2. A-level courses (2.5 credits)
CSCA08H3 Introduction to Computer Science I
MATA22H3 Linear Algebra I or Mathematical Sciences
MATA31H3* Calculus I for Mathematical Sciences
MATA37H3* Calculus II for Mathematical Sciences
[MATA67H3 or CSCA67H3 Discrete Mathematics]

3. B-level courses (2.5 credits)
MATB24H3 Linear Algebra II
MATB41H3 Techniques of the Calculus of Several Variables I
MATB61H3 Linear Programming and Optimization
STAB52H3 Introduction to Probability
STAB57H3 Introduction to Statistics

4. C-level courses (1.5 credits)
CSCC37H3 Introduction to Numerical Algorithms for Computational Mathematics
STAC62H3 Probability and Stochastic Processes I
STAC67H3 Regression Analysis

5. D-level courses (0.5 credit)
STAD37H3 Multivariate Analysis


A. Quantitative Finance Stream
This stream requires a total of 26 courses (13.0 credits). In addition to the core requirements, 11 other courses (5.5 credits) must be taken satisfying all of the following requirements:

6. Additional A-level courses (0.5 credit)
MGEA02H3 Introduction to Microeconomics: A Mathematical Approach

7. Additional B-level courses (2.0 credits)
ACTB40H3 Fundamentals of Investment and Credit
MATB42H3 Techniques of Calculus of Several Variables II
MATB44H3 Differential Equations I
STAB41H3 Financial Derivatives

8. Additional Upper-Level courses (3.0 credits)
MATC46H3 Differential Equations II
STAC70H3 Statistics and Finance I
STAD57H3 Time Series Analysis
STAD70H3 Statistics and Finance II
and
1.0 credit from the following:
CSCC11H3 Introduction to Machine Learning and Data Mining
MATC37H3 Introduction to Real Analysis
STAC51H3 Categorical Data Analysis
STAC58H3 Statistical Inference
STAC63H3 Probability and Stochastic Processes II
STAD68H3 Advanced Machine Learning and Data Mining
STAD92H3 Readings in Statistics
STAD93H3 Readings in Statistics
STAD94H3 Statistics Project
STAD95H3 Statistics Project
APM462H1 Nonlinear Optimization
Note: Students enrolled in this stream should also consider taking complementary courses in economics and finance (e.g. MGEA06H3, MGEB02H3, MGEB06H3, MGEC72H3), or the Minor in Economics for Management Studies.


B. Statistical Machine Learning and Data Science Stream
This stream requires a total of 26 courses (13.0 credits). In addition to the core requirements, 11 other courses (5.5 credits) must be taken satisfying all of the following requirements:

6. Additional A-level courses (0.5 credit)
CSCA48H3 Introduction to Computer Science II

7. Additional B-level courses (2.0 credits)
CSCB07H3 Software Design
[CSCB20H3 Introduction to Databases and Web Applications or STAA57H3 Introduction to Data Science]
CSCB36H3 Introduction to the Theory of Computation
CSCB63H3 Design and Analysis of Data Structures

8. Additional Upper Level courses (3.0 credits)
CSCC11H3 Introduction to Machine Learning and Data Mining
STAC58H3 Statistical Inference
[STAD68H3 Advanced Machine Learning and Data Mining or STAD78H3 Machine Learning Theory]
and
1.5 credits from the following (*):
Any C or D-level CSC, MAT or STA courses, excluding: STAC32H3, STAC53H3 and STAD29H3, 1.0 credit must be STA courses.
(*) Some of the courses on this list have prerequisites that are not included in this program; in choosing courses to satisfy this requirement, check the prerequisites carefully and plan accordingly.

C. Statistical Science Stream
This stream requires a total of 26 courses (13.0 credits). In addition to the core requirements, 11 other courses (5.5 credits) must be taken satisfying all of the following requirements:

6. Additional A-level courses (0.5 credit)
STAA57H3 Introduction to Data Science

7. Additional B-level courses (1.0 credit)
MATB42H3 Techniques of Calculus of Several Variables II
MATB44H3 Differential Equations I

8. Additional C-level courses (2.5 credits)
STAC33H3 Introduction to Applied Statistics
STAC50H3 Data Collection
STAC51H3 Categorical Data Analysis
STAC58H3 Statistical Inference
STAC63H3 Probability and Stochastic Processes II

9. Additional C- and D-level courses (1.0 credit)*
1.0 credit from the following:
CSCC11H3 Introduction to Machine Learning and Data Mining
MATC34H3 Complex Variables
MATC37H3 Introduction to Real Analysis (strongly recommended for students who wish to pursue graduate studies)
STAD68H3 Advanced Machine Learning and Data Mining
STAD78H3 Machine Learning Theory
STAD80H3 Analysis of Big Data
STAD92H3 Readings in Statistics
STAD93H3 Readings in Statistics
STAD94H3 Statistics Project
STAD95H3 Statistics Project
*Students should plan ahead when taking these courses to ensure that prerequisites are satisfied and, in the case of STAD92H3, STAD93H3, STAD94H3, and STAD95H3, that a faculty member has agreed to supervise the course (as this is not guaranteed).

10. Additional D-level courses (0.5 credit)
STAD57H3 Time Series Analysis

SPECIALIST (CO-OPERATIVE) PROGRAM IN STATISTICS (SCIENCE)

Academic Program Advisor: S. Calanza  susan.calanza@utoronto.ca
Co-op Program Coordinator: C. Dixon  coopsuccess.utsc@utoronto.ca

The Specialist (Co-operative) Program in Statistics is a Work Integrated Learning (WIL) program that combines academic studies with paid work terms in the public, private, and/or non-profit sectors. The program provides students with the opportunity to develop the academic and professional skills required to pursue employment in these areas, or to continue on to graduate training in an academic field related to Statistics upon graduation.

In addition to their academic course requirements, students must successfully complete the additive Arts & Science Co-op Work Term and Course requirements.

Enrolment Requirements

Enrolment in the Specialist (Co-operative) Program in Statistics is limited.

Current Co-op Students:
Students admitted to a Co-op Degree POSt in their first year of study must request a Co-op Subject POSt on ACORN upon completion of 4.0 credits. Students must have completed the required A-level CSC and MAT courses, and achieved the required grades, described in the Enrolment Requirements for the Specialist in Statistics. In addition, they must also have received a CGPA of at least 2.5 across all attempted courses.

Prospective Co-op Students:
Prospective students (i.e., those not yet admitted to a Co-op Degree POSt) must meet the enrolment requirements noted above and have a CGPA of at least 2.75 across all attempted courses.

Prospective students must request the Co-op program on ACORN. Request deadlines follow the Limited Enrolment Program Application Deadlines set by the Office of the Registrar each year. Failure to make the program request on ACORN will result in the student's application not being considered.

Academic Program Requirements
Students must complete the program requirements as described in the Specialist Program in Statistics.

Co-op Work Term Requirements
Students must satisfactorily complete Co-op work term(s), as follows: three 4-month work terms, one 4-month work term and one 8-month work term, or one 12-month work term. To be eligible for their first work term, students must be enrolled in the Specialist (Co-op) Program in Statistics and have completed at least 7.0 credits, achieve a cumulative GPA of 2.5 or higher, and complete COPB50H3 and COPB51H3.

Students must be available for work terms in each of the Fall, Winter and Summer semesters and must complete at least one of their required work terms in either a Fall or Winter semester. This requires that students take courses during at least one Summer semester.

Co-op Course Requirements
In addition to their academic program requirements, Co-op students complete the following Co-op specific courses as part of their degree:

  • Co-op Preparation courses: COPB50H3 and COPB51H3 (completed in first year)
  • Work Term Search courses: COPB52H3 (semester prior to first work term), COPC98H3 (semester prior to second work term), and COPC99H3 (semester prior to third work term)
  • Co-op Work Term courses: COPC01H3 (each semester a student is on work term)

These courses are designed to prepare students for their job search and work term experience, and to maximize the benefits of their Co-op work terms. They must be completed in sequence, and fall into three categories: Co-op Preparation courses (COPB50H3 & COPB51H3) are completed in first year, and cover a variety of topics intended to assist students in developing the skills and tools required to secure a work term; Work Term Search Courses (COPB52H3, COPC98H3, & COPC99H3) are completed in the semester prior to each work term, and support students while competing for work terms that are appropriate to their program of study, as well as preparing students for the transition into and how to succeed the workplace; Co-op Work Term courses (COPC01H3) are completed during each semester that a student is on work term, and support students’ success while on work term, as well as connecting their academics and the workplace experience.

Co-op courses are taken in addition to a full course load. They are recorded on transcripts as credit/no credit (CR/NCR) and are considered to be additive credit to the 20.0 required degree credits. No additional course fee is assessed as registration is included in the Co-op Program fee.

For information on fees, status in Co-op programs, and certification of completion of Co-op programs, see the 6B.5 Co-operative Programs section or the Arts and Science Co-op section in the UTSC Calendar.

MAJOR PROGRAM IN STATISTICS (SCIENCE)

Supervisor of Studies: M. Samarakoon Email: mahinda.samarakoon@utoronto.ca

Recommended Writing Course
Students are urged to take a course from the following list of courses by the end of their second year. ANTA01H3, ANTA02H3, CLAA06H3, (CTLA19H3), CTLA01H3, ENGA10H3, ENGA11H3, ENGB06H3, ENGB07H3, ENGB08H3, ENGB09H3, ENGB17H3, ENGB19H3, ENGB50H3, (ENGB51H3), GGRA02H3, GGRA03H3, GGRB05H3, (GGRB06H3), (HISA01H3), (HLTA01H3), ACMA01H3, (HUMA01H3), (HUMA11H3), (HUMA17H3), (LGGA99H3), LINA01H3, PHLA10H3, PHLA11H3, WSTA01H3.

Enrolment Requirements

Enrolment in the Major Program in Statistics is limited.

Students may apply to enter the program after completing 4.0 credits, and must meet the requirements described below:

1. Students already admitted to the UTSC Year 1 Statistics admissions category:

Required Courses:

Students must have passed the following CSC and MAT courses:[CSCA08H3 or CSCA20H3], MATA22H3, [MATA30H3 or MATA31H3] and [MATA36H3 or MATA37H3].

Required Grades:

Students that meet the following requirements will be admitted to the Statistics Major POSt:
a. A cumulative grade point average (CGPA) of at least 2.3 over the following courses: CSCA08H3/​CSCA20H3, MATA22H3, MATA30/31H3, and MATA36/37H3.

2. Students admitted to other UTSC Year 1 admissions categories:

Students that have been admitted to other CMS admissions categories (Computer Science or Mathematics) or any other UTSC Year 1 admissions categories are eligible to apply for a Statistics Major POSt. Admission will be based on academic performance in the required A-level courses, identified above. The admission requirements change each year depending on available spaces and the pool of eligible applicants, and students are cautioned that there is no guarantee of admission; as such, students are strongly advised to plan to enroll in backup programs.

For more information about the admission requirements, please visit the following CMS webpage.

Program Requirements
This program requires 8.0 credits.

1. A-level courses
MATA22H3 Linear Algebra I for Mathematical Sciences
[CSCA08H3 Introduction to Computer Science I or CSCA20H3 Computer Science for the Sciences]
[MATA30H3 Calculus I for Physical Sciences or MATA31H3 Calculus I for Mathematical Sciences*]
[MATA36H3 Calculus II for Physical Sciences or MATA37H3 Calculus II for Mathematical Sciences*]
*The sequence MATA31H3 and MATA37H3 is recommended. MATA31H3 is the prerequisite for MATA37H3.

2. B-level courses
MATB24H3 Linear Algebra II
MATB41H3 Techniques of the Calculus of Several Variables I
MATB42H3 Techniques of the Calculus of Several Variables II
[STAB52H3 An Introduction to Probability or STAB53H3 Introduction to Applied Probability]*
STAB57H3 An Introduction to Statistics*

3. Upper-level courses
STAC67H3 Regression Analysis*
and
1.5 credits from the following:
any C- or D-level STA courses, except: STAC32H3, STAC53H3 and STAD29H3

4. Electives
1.5 credit from the following:
STAA57H3, ACTB40H3, STAB41H3, MATB61H3, or any C- or D-level CSC, MAT or STA courses

* STAB52H3, STAB53H3, STAB57H3, STAC67H3 - These courses must be taken at UTSC. No substitutes are permitted without permission from the program supervisor.

MAJOR (CO-OPERATIVE) PROGRAM IN STATISTICS (SCIENCE)

Academic Program Advisor: S. Calanza  susan.calanza@utoronto.ca
Co-op Program Coordinator: C. Dixon  coopsuccess.utsc@utoronto.ca

The Major (Co-op) Program in Statistics is a Work Integrated Learning (WIL) program that combines academic studies with paid work terms in the public, private, and/or non-profit sectors. The program provides students with the opportunity to develop the academic and professional skills required to pursue employment in these areas, or to continue on to graduate training in an academic field related to Statistics upon graduation.
In addition to their academic course requirements, students must successfully complete the additive Arts & Science Co-op Work Term and Course requirements.

Enrolment Requirements

Enrolment in the Major (Co-operative) Program in Statistics is limited.

Current Co-op Students:
Students admitted to a Co-op Degree POSt in their first year of study must request a Co-op Subject POSt on ACORN upon completion of 4.0 credits. Students must have passed the required A-level CSC and MAT courses, and achieved the required grades, described in the Enrolment Requirements for the Major in Statistics. In addition, they must also have achieved a CGPA of at least 2.5 across all attempted courses.

Prospective Co-op Students:
Prospective students (i.e., those not yet admitted to a Co-op Degree POSt) must meet the enrolment requirements noted above and have a CGPA of at least 2.5 across all attempted courses.

Students must submit a program request on ACORN. Deadlines follow the Limited Enrolment Program Application Deadlines set by the Office of the Registrar each year. Failure to submit the program request on ACORN will result in the student's application not being considered.

Academic Program Requirements
Students must complete the program requirements as described in the Major Program in Statistics.

Co-op Work Term Requirements
Students must satisfactorily complete Co-op work term(s) as follows: three 4-month work terms, one 4-month work term and one 8-month work term, or one 12-month work term. To be eligible for their first work term, students must be enrolled in the Major (Co-op) Program in Statistics and have completed at least 7.0 credits, achieve a cumulative GPA of 2.5 or higher, and complete COPB50 and COPB51.

Students must be available for work terms in each of the Fall, Winter, and Summer semesters and must complete at least one of their required work terms in either a Fall or Winter semester. This requires that students take courses during at least one Summer semester.

Co-op Course Requirements
In addition to their academic program requirements, Co-op students complete the following Co-op specific courses as part of their degree:

  • Co-op Preparation courses: COPB50H3 and COPB51H3 (completed in first year)
  • Work Term Search courses: COPB52H3 (semester prior to first work term), COPC98H3 (semester prior to second work term), and COPC99H3 (semester prior to third work term)
  • Co-op Work Term courses: COPC01H3 (each semester a student is on work term)

These courses are designed to prepare students for their job search and work term experience, and to maximize the benefits of their Co-op work terms. They must be completed in sequence, and fall into three categories: Co-op Preparation courses (COPB50H3 & COPB51H3) are completed in first year, and cover a variety of topics intended to assist students in developing the skills and tools required to secure a work term; Work Term Search Courses (COPB52H3, COPC98H3, & COPC99H3) are completed in the semester prior to each work term, and support students while competing for work terms that are appropriate to their program of study, as well as preparing students for the transition into and how to succeed the workplace; Co-op Work Term courses (COPC01H3) are completed during each semester that a student is on work term, and support students’ success while on work term, as well as connecting their academics and the workplace experience.

Co-op courses are taken in addition to a full course load. They are recorded on transcripts as credit/no credit (CR/NCR) and are considered to be additive credit to the 20.0 required degree credits. No additional course fee is assessed as registration is included in the Co-op Program fee.

For information on fees, status in Co-op programs, and certification of completion of Co-op programs, see the 6B.5 Co-operative Programs section or the Arts and Science Co-op section in the UTSC Calendar.

MINOR PROGRAM IN APPLIED STATISTICS (SCIENCE)

Supervisor of Studies: K. Butler Email: ken.butler@utoronto.ca

The Minor in Applied Statistics may be combined with the Minor in Computer Science; however, it cannot be combined with any Specialist/Specialist Co-op, Major/Major Co-op or other Minor programs in Computer Science, Mathematics or Statistics.

Program Requirements
This program requires a total of 4.0 credits as follows:

1. 0.5 credit from the following:
CSCA08H3 Introduction to Computer Science I
CSCA20H3 Introduction to Programming
CSC120H1 Computer Science for the Sciences
CSC121H1 Computer Science for Statistics

2. 0.5 credit from the following:
STAB22H3 Statistics I
STAB23H3 Introduction to Statistics for the Social Sciences
MGEB11H3 Quantitative Methods in Economics I
PSYB07H3 Data Analysis in Psychology
STA220H1 The Practice of Statistics I

3. 0.5 credit from the following:
STAB27H3 Statistics II
MGEB12H3 Quantitative Methods in Economics II
PSYC08H3 Advanced Data Analysis in Psychology
STA221H1 The Practice of Statistics II

4. 1.5 credits as follows:
STAC32H3 Applications of Statistical Methods
STAC53H3 Applied Data Collection
STAD29H3 Statistics for Life and Social Scientists

5. 1.0 credit from the following:
[one of the following: any ACT, CSC, MAT, STA course]
[one of the following: MGEA02H3, MGEA06H3, MGEB02H3, MGEB06H3, MGEC11H3, MGED11H3, MGED70H3]
GGRB02H3 The Logic of Geographical Thought
HLTB15H3 Introduction to Health Research Methodology
[one of the following: MGFB10H3, MGFC30H3, MGOC10H3, MGMC01H3, MGMD01H3]
POLC11H3 Applied Statistics for Politics and Public Policy

MINOR PROGRAM IN STATISTICS (SCIENCE)

Supervisor of Studies: M. Samarakoon Email: mahinda@utsc.utoronto.ca

Program Requirements
This program requires 4.0 credits.

1. First Year (2.0 credits)
[CSCA08H3 Introduction to Computer Science I or CSCA20H3 Computer Science for the Sciences]
MATA23H3 Linear Algebra I
[MATA30H3 Calculus I for Physical Sciences or MATA31H3 Calculus I for Mathematical Sciences] and
[MATA36H3 Calculus II for Physical Sciences or MATA37H3 Calculus II for Mathematical Sciences.]
Notes:
1. The sequence [MATA31H3 and MATA37H3] is recommended.
2. MATA31H3 is the pre-requisite for MATA37H3.

2. Second Year (1.0 credit)
[STAB52H3 An Introduction to Probability or STAB53H3 Introduction to Applied Probability]
STAB57H3 An Introduction to Statistics 

3. Third and Fourth Year (0.5 credit)
STAC67H3 Regression Analysis

4. In addition, 0.5 credit must be chosen from any C- or D-level STA course (excluding STAC32H3, STAC53H3 and STAD29H3).

Statistics Courses

ACTB40H3 - Fundamentals of Investment and Credit

This course is concerned with the concept of financial interest. Topics covered include: interest, discount and present values, as applied to determine prices and values of annuities, mortgages, bonds, equities, loan repayment schedules and consumer finance payments in general, yield rates on investments given the costs on investments.

Prerequisite: MATA30H3 or MATA31H3 or MATA34H3
Exclusion: ACT240H, MGFB10H3/(MGTB09H3), (MGTC03H3)
Breadth Requirements: Quantitative Reasoning
Note: Students enrolled in or planning to enrol in any of the B.B.A. programs are strongly urged not to take ACTB40H3 because ACTB40H3 is an exclusion for MGFB10H3/(MGTB09H3)/(MGTC03H3), a required course in the B.B.A. degree. Students in any of the B.B.A programs will thus be forced to complete MGFB10H3/(MGTB09H3)/(MGTC03H3), even if they have credit for ACTB40H3, but will only be permitted to count one of ACTB40H3 and MGFB10H3/(MGTB09H3)/(MGTC03H3) towards the 20 credits required to graduate.

STAA57H3 - Introduction to Data Science

Reasoning using data is an integral part of our increasingly data-driven world. This course introduces students to statistical thinking and equips them with practical tools for analyzing data. The course covers the basics of data management and visualization, sampling, statistical inference and prediction, using a computational approach and real data.

Prerequisite: CSCA08H3
Exclusion: STAB22H3, STA130H, STA220H
Breadth Requirements: Quantitative Reasoning
Course Experience: Partnership-Based Experience

STAB22H3 - Statistics I

This course is a basic introduction to statistical reasoning and methodology, with a minimal amount of mathematics and calculation. The course covers descriptive statistics, populations, sampling, confidence intervals, tests of significance, correlation, regression and experimental design. A computer package is used for calculations.

Exclusion: ANTC35H3, MGEB11H3/(ECMB11H3), (POLB11H3), PSYB07H3, (SOCB06H3), STAB23H3, STAB52H3, STAB57H3, STA220H, (STA250H)
Breadth Requirements: Quantitative Reasoning

STAB23H3 - Introduction to Statistics for the Social Sciences

This course covers the basic concepts of statistics and the statistical methods most commonly used in the social sciences. The first half of the course introduces descriptive statistics, contingency tables, normal probability distribution, and sampling distributions. The second half of the course introduces inferential statistical methods. These topics include significance test for a mean (t-test), significance test for a proportion, comparing two groups (e.g., comparing two proportions, comparing two means), associations between categorical variables (e.g., Chi-square test of independence), and simple linear regression.

Exclusion: ANTC35H3, MGEB11H3/(ECMB11H3), (POLB11H3), PSYB07H3, (SOCB06H3), STAB22H3, STAB52H3, STAB57H3, STA220H, STA250H
Breadth Requirements: Quantitative Reasoning

STAB27H3 - Statistics II

This course follows STAB22H3, and gives an introduction to regression and analysis of variance techniques as they are used in practice. The emphasis is on the use of software to perform the calculations and the interpretation of output from the software. The course reviews statistical inference, then treats simple and multiple regression and the analysis of some standard experimental designs.

Prerequisite: STAB22H3 or STAB23H3
Exclusion: MGEB12H3/(ECMB12H3), STAB57H3, STA221H, (STA250H)
Breadth Requirements: Quantitative Reasoning

STAB41H3 - Financial Derivatives

A study of the most important types of financial derivatives, including forwards, futures, swaps and options (European, American, exotic, etc). The course illustrates their properties and applications through examples, and introduces the theory of derivatives pricing with the use of the no-arbitrage principle and binomial tree models.

Prerequisite: ACTB40H3 or MGFB10H3
Exclusion: MGFC30H3/(MGTC71H3)
Breadth Requirements: Quantitative Reasoning

STAB52H3 - An Introduction to Probability

A mathematical treatment of probability. The topics covered include: the probability model, density and distribution functions, computer generation of random variables, conditional probability, expectation, sampling distributions, weak law of large numbers, central limit theorem, Monte Carlo methods, Markov chains, Poisson processes, simulation, applications. A computer package will be used.

Prerequisite: MATA22H3 and MATA37H3
Exclusion: STAB53H3, PSYB07H3, STA107H, STA237H1, STA247H1, STA257H, STA246H5, STA256H5
Breadth Requirements: Quantitative Reasoning

STAB53H3 - Introduction to Applied Probability

An introduction to probability theory with an emphasis on applications in statistics and the sciences. Topics covered include probability spaces, random variables, discrete and continuous probability distributions, expectation, conditional probability, limit theorems, and computer simulation.

Prerequisite: [MATA22H3 or MATA23H3] and [MATA35H3 or MATA36H3 or MATA37H3]
Exclusion: STAB52H3, PSYB07H3, STA107H, STA237H1, STA247H1, STA257H, STA246H5, STA256H5
Breadth Requirements: Quantitative Reasoning
Course Experience: University-Based Experience

STAB57H3 - An Introduction to Statistics

A mathematical treatment of the theory of statistics. The topics covered include: the statistical model, data collection, descriptive statistics, estimation, confidence intervals and P-values, likelihood inference methods, distribution-free methods, bootstrapping, Bayesian methods, relationship among variables, contingency tables, regression, ANOVA, logistic regression, applications. A computer package will be used.

Prerequisite: [STAB52H3 or STAB53H3]
Exclusion: MGEB11H3, PSYB07H3, STAB22H3, STAB23H3, STA220H1, STA261H
Breadth Requirements: Quantitative Reasoning

STAC32H3 - Applications of Statistical Methods

A case-study based course, aimed at developing students’ applied statistical skills beyond the basic techniques. Students will be required to write statistical reports. Statistical software, such as SAS and R, will be taught and used for all statistical analyses.

Prerequisite: STAB27H3 or MGEB12H3 or PSYC08H3 or STA221H1
Exclusion: STAC33H3
Breadth Requirements: Quantitative Reasoning

STAC33H3 - Introduction to Applied Statistics

This course introduces students to statistical software, such as R and SAS, and its use in analyzing data. Emphasis will be placed on communication and explanation of findings. Students will be required to write a statistical report.

Prerequisite: STAB57H3 or STA248H3 or STA261H3
Exclusion: STAC32H3
Breadth Requirements: Quantitative Reasoning

STAC50H3 - Data Collection

The principles of proper collection of data for statistical analysis, and techniques to adjust statistical analyses when these principles cannot be implemented. Topics include: relationships among variables, causal relationships, confounding, random sampling, experimental designs, observational studies, experiments, causal inference, meta-analysis. Statistical analyses using SAS or R.

Students enrolled in the Minor program in Applied Statistics should take STAC53H3 instead.

Prerequisite: STAB57H3 or STA261H1. Students enrolled in the Minor program in Applied Statistics should take STAC53H3.
Exclusion: STA304H, STAC53H3
Breadth Requirements: Quantitative Reasoning

STAC51H3 - Categorical Data Analysis

Statistical models for categorical data. Contingency tables, generalized linear models, logistic regression, multinomial responses, logit models for nominal responses, log-linear models for two-way tables, three-way tables and higher dimensions, models for matched pairs, repeated categorical response data, correlated and clustered responses. Statistical analyses using SAS or R.

Prerequisite: STAB27H3 or STAB57H3 or MGEB12H3 or PSYC08H3
Exclusion: STA303H1
Breadth Requirements: Quantitative Reasoning

STAC53H3 - Applied Data Collection

This course introduces the principles, objectives and methodologies of data collection. The course focuses on understanding the rationale for the various approaches to collecting data and choosing appropriate statistical techniques for data analysis. Topics covered include elements of sampling problems, simple random sampling, stratified sampling, ratio, regression, and difference estimation, systematic sampling, cluster sampling, elements of designed experiments, completely randomized design, randomized block design, and factorial experiments. The R statistical software package is used to illustrate statistical examples in the course. Emphasis is placed on the effective communication of statistical results.

Prerequisite: STAB27H3 or MGEB12H3 or PSYC08H3 or STA221H1
Exclusion: STAC50H3, STA304H1, STA304H5
Breadth Requirements: Quantitative Reasoning
Note: Students enrolled in the Specialist or Major programs in Statistics should take STAC50H3.

STAC58H3 - Statistical Inference

Principles of statistical reasoning and theories of statistical analysis. Topics include: statistical models, likelihood theory, repeated sampling theories of inference, prior elicitation, Bayesian theories of inference, decision theory, asymptotic theory, model checking, and checking for prior-data conflict. Advantages and disadvantages of the different theories.

Prerequisite: STAB57H3 and STAC62H3
Exclusion: STA352Y, STA422H
Breadth Requirements: Quantitative Reasoning

STAC62H3 - Probability and Stochastic Processes I

This course continues the development of probability theory begun in STAB52H3. Topics covered include finite dimensional distributions and the existence theorem, discrete time Markov chains, discrete time martingales, the multivariate normal distribution, Gaussian processes and Brownian motion.

Prerequisite: MATB41H3 and STAB52H3
Exclusion: STA347H1
Breadth Requirements: Quantitative Reasoning

STAC63H3 - Probability and Stochastic Processes II

This course continues the development of probability theory begun in STAC62H3. Probability models covered include branching processes, birth and death processes, renewal processes, Poisson processes, queuing theory, random walks and Brownian motion.

Prerequisite: STAC62H3
Exclusion: STA447H1, STA348H5
Breadth Requirements: Quantitative Reasoning

STAC67H3 - Regression Analysis

Orthogonal projections. Univariate normal distribution theory. The linear model and its statistical analysis, residual analysis, influence analysis, collinearity analysis, model selection procedures. Analysis of designs. Random effects. Models for categorical data. Nonlinear models. Instruction in the use of SAS.

Prerequisite: STAB57H3
Exclusion: STA302H; [Students who want to complete both STAC67H3 and MGEB12H3, and receive credit for both courses, must successfully complete MGEB12H3 prior to enrolling in STAC67H3; for students who complete MGEB12H3 after successfully completing STAC67H3, MGEB12H3 will be marked as Extra (EXT)]
Breadth Requirements: Quantitative Reasoning

STAC70H3 - Statistics and Finance I

A mathematical treatment of option pricing. Building on Brownian motion, the course introduces stochastic integrals and Itô calculus, which are used to develop the Black-Scholes framework for option pricing. The theory is extended to pricing general derivatives and is illustrated through applications to risk management.

Prerequisite: [STAB41H3 or MGFC30H3/(MGTC71H3)] and STAC62H3
Corequisite: MATC46H3
Exclusion: APM466H, ACT460H
Breadth Requirements: Quantitative Reasoning

STAD29H3 - Statistics for Life & Social Scientists

The course discusses many advanced statistical methods used in the life and social sciences. Emphasis is on learning how to become a critical interpreter of these methodologies while keeping mathematical requirements low. Topics covered include multiple regression, logistic regression, discriminant and cluster analysis, principal components and factor analysis.

Prerequisite: STAC32H3
Exclusion: All C-level/300-level and D-level/400-level STA courses or equivalents except STAC32H3, STAC53H3, STAC51H3 and STA322H.
Breadth Requirements: Quantitative Reasoning

STAD37H3 - Multivariate Analysis

Linear algebra for statistics. Multivariate distributions, the multivariate normal and some associated distribution theory. Multivariate regression analysis. Canonical correlation analysis. Principal components analysis. Factor analysis. Cluster and discriminant analysis. Multidimensional scaling. Instruction in the use of SAS.

Prerequisite: STAC67H3
Exclusion: STA437H, (STAC42H3)
Breadth Requirements: Quantitative Reasoning

STAD57H3 - Time Series Analysis

An overview of methods and problems in the analysis of time series data. Topics covered include descriptive methods, filtering and smoothing time series, identification and estimation of times series models, forecasting, seasonal adjustment, spectral estimation and GARCH models for volatility.

Prerequisite: STAC62H3 and STAC67H3
Exclusion: STA457H, (STAC57H3)
Breadth Requirements: Quantitative Reasoning

STAD68H3 - Advanced Machine Learning and Data Mining

Statistical aspects of supervised learning: regression, regularization methods, parametric and nonparametric classification methods, including Gaussian processes for regression and support vector machines for classification, model averaging, model selection, and mixture models for unsupervised learning. Some advanced methods will include Bayesian networks and graphical models.

Prerequisite: CSCC11H3 and STAC58H3 and STAC67H3
Breadth Requirements: Quantitative Reasoning

STAD70H3 - Statistics and Finance II

A survey of statistical techniques used in finance. Topics include mean-variance and multi-factor analysis, simulation methods for option pricing, Value-at-Risk and related risk-management methods, and statistical arbitrage. A computer package will be used to illustrate the techniques using real financial data.

Prerequisite: STAC70H3 and STAD37H3
Corequisite: STAD57H3
Breadth Requirements: Quantitative Reasoning

STAD78H3 - Machine Learning Theory

Presents theoretical foundations of machine learning. Risk, empirical risk minimization, PAC learnability and its generalizations, uniform convergence, VC dimension, structural risk minimization, regularization, linear models and their generalizations, ensemble methods, stochastic gradient descent, stability, online learning.

Prerequisite: STAB57H3 and STAC62H3
Recommended Preparation: STAC58H3 and STAC67H3
Breadth Requirements: Quantitative Reasoning

STAD80H3 - Analysis of Big Data

Big data is transforming our world, revolutionizing operations and analytics everywhere, from financial engineering to biomedical sciences. Big data sets include data with high-dimensional features and massive sample size. This course introduces the statistical principles and computational tools for analyzing big data: the process of acquiring and processing large datasets to find hidden patterns and gain better understanding and prediction, and of communicating the obtained results for maximal impact. Topics include optimization algorithms, inferential analysis, predictive analysis, and exploratory analysis.

Prerequisite: STAC58H3 and STAC67H3 and CSCC11H3
Breadth Requirements: Quantitative Reasoning

STAD81H3 - Causal Inference

Correlation does not imply causation. Then, how can we make causal claims? To answer this question, this course introduces theoretical foundations and modern statistical and graphical tools for making causal inference. Topics include potential outcomes and counterfactuals, measures of treatment effects, causal graphical models, confounding adjustment, instrumental variables, principal stratification, mediation and interference.

Prerequisite: STAC50H3 and STAC58H3 and STAC67H3
Enrolment Limits: 40
Breadth Requirements: Quantitative Reasoning

STAD91H3 - Topics in Statistics

Topics of interest in Statistics, as selected by the instructor. The exact topics can vary from year to year. Enrolment is by permission of the instructor only.

Prerequisite: Permission from the instructor is required. This will typically require the completion of specific courses which can vary from year to year.
Breadth Requirements: Quantitative Reasoning
Course Experience: University-Based Experience

STAD92H3 - Readings in Statistics

This course is offered by arrangement with a statistics faculty member who must agree to supervise. This course may be taken in any session and must be completed by the last day of classes in the session in which it is taken.

Prerequisite: Students must obtain consent from the Supervisor of Studies before registering for this course.
Breadth Requirements: Quantitative Reasoning

STAD93H3 - Readings in Statistics

This course is offered by arrangement with a statistics faculty member who must agree to supervise. This course may be taken in any session and must be completed by the last day of classes in the session in which it is taken.

Prerequisite: Students must obtain consent from the Supervisor of Studies before registering for this course.
Breadth Requirements: Quantitative Reasoning

STAD94H3 - Statistics Project

A significant project in any area of statistics. The project may be undertaken individually or in small groups. This course is offered by arrangement with a statistics faculty member who must agree to supervise. This course may be taken in any session and the project must be completed by the last day of classes in the session in which it is taken.

Prerequisite: Students must obtain consent from the Supervisor of Studies before registering for this course.
Breadth Requirements: Quantitative Reasoning

STAD95H3 - Statistics Project

A significant project in any area of statistics. The project may be undertaken individually or in small groups. This course is offered by arrangement with a statistics faculty member who must agree to supervise. This course may be taken in any session and the project must be completed by the last day of classes in the session in which it is taken.

Prerequisite: Students must obtain consent from the Supervisor of Studies before registering for this course.
Breadth Requirements: Quantitative Reasoning

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