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