Summer Sessions | Courses | Statistics

Statistics

The Statistics Department offers a range of courses that build on a foundation in probability and statistical theory to provide practical training in statistical methods, study design, and data analysis.

Check the Directory of Classes for the most up-to-date course information.

Summer 2022 Session Information

  • SESSION A (First Half Term) courses are May 23–July 1, 2022
  • SESSION B (Second Half Term) courses are July 5–August 12, 2022
  • SESSION X (Full Term) courses are May 23–August 12, 2022

Visit our calendar for a complete list of Summer dates.

Courses
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INTRO TO STATISTICAL REASONING
STAT1001W001 3 points.

A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance.

Course Number Section/Call Number Session Times/Location
STAT1001W001 001/10120 Session B Mo 10:45 AM–12:20 PM
Tu 10:45 AM–12:20 PM
We 10:45 AM–12:20 PM
Th 10:45 AM–12:20 PM

Instructor Points Enrollment Method of Instruction
Gabriel Young
3 Open for Enrollment
(auto-fill Wait List)
In-Person
INTRODUCTION TO STATISTICS
STAT1101S001 3 points.
Prerequisites: some high school algebra. Designed for students in fields that emphasize quantitative methods. This course satisfies the statistics requirements of all majors except statistics, economics, and engineering. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, confidence intervals, and hypothesis testing are taught as aids to quantitative reasoning and data analysis. Use of statistical software required. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.
Course Number Section/Call Number Session Times/Location
STAT1101S001 001/10121 Session A Mo 10:45 AM–12:20 PM
Tu 10:45 AM–12:20 PM
We 10:45 AM–12:20 PM
Th 10:45 AM–12:20 PM

Instructor Points Enrollment Method of Instruction
Anthony Donoghue
3 Open for Enrollment
(auto-fill Wait List)
In-Person
INTRODUCTION TO STATISTICS
STAT1101S002 3 points.
Prerequisites: some high school algebra. Designed for students in fields that emphasize quantitative methods. This course satisfies the statistics requirements of all majors except statistics, economics, and engineering. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, confidence intervals, and hypothesis testing are taught as aids to quantitative reasoning and data analysis. Use of statistical software required. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.
Course Number Section/Call Number Session Times/Location
STAT1101S002 002/10122 Session B Mo 06:15 PM–07:50 PM
Tu 06:15 PM–07:50 PM
We 06:15 PM–07:50 PM
Th 06:15 PM–07:50 PM

Instructor Points Enrollment Method of Instruction
Arnab Auddy
3 Open for Enrollment
(auto-fill Wait List)
In-Person
CALC-BASED INTRO TO STATISTICS
STAT1201S001 3 points.
Prerequisites: working knowledge of calculus (differentiation and integration). Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value,confidence intervals. maximum likelihood estimation. Satisfies the pre-requisites for ECON W3412.
Course Number Section/Call Number Session Times/Location
STAT1201S001 001/10123 Session A Mo 10:45 AM–12:20 PM
Tu 10:45 AM–12:20 PM
We 10:45 AM–12:20 PM
Th 10:45 AM–12:20 PM

Instructor Points Enrollment Method of Instruction
Ji Meng Loh
3 Open for Enrollment
(auto-fill Wait List)
In-Person
CALC-BASED INTRO TO STATISTICS
STAT1201S002 3 points.
Prerequisites: working knowledge of calculus (differentiation and integration). Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value,confidence intervals. maximum likelihood estimation. Satisfies the pre-requisites for ECON W3412.
Course Number Section/Call Number Session Times/Location
STAT1201S002 002/10124 Session B Mo 06:15 PM–07:50 PM
Tu 06:15 PM–07:50 PM
We 06:15 PM–07:50 PM
Th 06:15 PM–07:50 PM

Instructor Points Enrollment Method of Instruction
Ye Tian
3 Open for Enrollment
(auto-fill Wait List)
In-Person
Undergraduate Mentored Research
STAT3107W001 3 points.

Prerequisites: the project mentors permission. This course provides a mechanism for students who undertake research with a faculty member from the Department of Statistics to receive academic credit. Students seeking research opportunities should be proactive and entrepreneurial: identify congenial faculty whose research is appealing, let them know of your interest and your background and skills.

Course Number Section/Call Number Session Times/Location
STAT3107W001 001/10459 Session A
Instructor Points Enrollment Method of Instruction
Ronald Neath
3 Open for Enrollment
(auto-fill Wait List)
In-Person
INTRODUCTION TO PROBABILITY AND STATISTICS
STAT4001S001 3 points.

Prerequisites: A good working knowledge of calculus, including derivatives, single and double, limits, sums and series. Life is a gamble and with some knowledge of probability / statistics is easier evaluate the risks and rewards involved. Probability theory allows us take a known underlying model and estimate how likely will we be able to see future events. Statistical Inference allows us to take data we have seen and estimate the missing parts of an unknown model. The first part of the course focus on the former and the second part the latter.

Course Number Section/Call Number Session Times/Location
STAT4001S001 001/10125 Session A Mo 06:15 PM–07:50 PM
Tu 06:15 PM–07:50 PM
We 06:15 PM–07:50 PM
Th 06:15 PM–07:50 PM

Instructor Points Enrollment Method of Instruction
Tat Sang Fung
3 Open for Enrollment
(auto-fill Wait List)
In-Person
PROBABILITY THEORY
STAT4203S001 3 points.
Prerequisites: MATH V1101 Calculus I and MATH V1102 Calculus II, or the equivalent, and STAT W1111 or STAT W1211 (Introduction to Statistics). Corequisites: MATH V1201 Calculus III, or the equivalent, or the instructor's permission. This course can be taken as a single course for students requiring knowledge of probability or as a foundation for more advanced courses. It is open to both undergraduate and master students. This course satisfies the prerequisite for STAT W3107 and W4107. Topics covered include combinatorics, conditional probability, random variables and common distributions, expectation, independence, Bayes' rule, joint distributions, conditional expectations, moment generating functions, central limit theorem, laws of large numbers, characteristic functions.
Course Number Section/Call Number Session Times/Location
STAT4203S001 001/10126 Session A Mo 04:30 PM–06:05 PM
Tu 04:30 PM–06:05 PM
We 04:30 PM–06:05 PM
Th 04:30 PM–06:05 PM

Instructor Points Enrollment Method of Instruction
Young Kim
3 Open for Enrollment
(auto-fill Wait List)
In-Person
STATISTICAL INFERENCE
STAT4204S001 3 points.
Prerequisites: STAT W3105 Intro. to Probability or STAT W4105 Probability, or the equivalent. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals, maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.
Course Number Section/Call Number Session Times/Location
STAT4204S001 001/10127 Session A Mo 06:15 PM–07:50 PM
Tu 06:15 PM–07:50 PM
We 06:15 PM–07:50 PM
Th 06:15 PM–07:50 PM

Instructor Points Enrollment Method of Instruction
David Rios
3 Open for Enrollment
(auto-fill Wait List)
In-Person
Linear Regression Models
STAT4205S001 3 points.

Prerequisites: STAT GU4204 or the equivalent, and a course in linear algebra. Theory and practice of regression analysis. Simple and multiple regression, testing, estimation, prediction, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

Course Number Section/Call Number Session Times/Location
STAT4205S001 001/10128 Session A Mo 06:15 PM–07:50 PM
Tu 06:15 PM–07:50 PM
We 06:15 PM–07:50 PM
Th 06:15 PM–07:50 PM

Instructor Points Enrollment Method of Instruction
Daniel Rabinowitz
3 Open for Enrollment
(auto-fill Wait List)
In-Person
STAT COMP & INTRO DATA SCIENCE
STAT4206S001 3 points.
Prerequisites: STAT GU4204 and GU4205 or the equivalent. Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.
Course Number Section/Call Number Session Times/Location
STAT4206S001 001/10129 Session A Mo 04:30 PM–06:05 PM
Tu 04:30 PM–06:05 PM
We 04:30 PM–06:05 PM
Th 04:30 PM–06:05 PM

Instructor Points Enrollment Method of Instruction
Gabriel Young
3 Open for Enrollment
(auto-fill Wait List)
In-Person
Time Series Analysis
STAT4221S001 3 points.
Prerequisites: STAT GU4205 or the equivalent. Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.
Course Number Section/Call Number Session Times/Location
STAT4221S001 001/10130 Session A Mo 01:00 PM–04:10 PM
We 01:00 PM–04:10 PM

Instructor Points Enrollment Method of Instruction
Rongning Wu
3 Open for Enrollment
(auto-fill Wait List)
In-Person
BAYESIAN STATISTICS
STAT4224W001 3 points.

This course introduces the Bayesian paradigm for statistical inference.  Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models; Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.

Prerequisites: A course in the theory of statistical inference, such as STAT GU4204 a course in statistical modeling and data analysis, such as STAT GU4205.

 

Course Number Section/Call Number Session Times/Location
STAT4224W001 001/10131 Session B Mo 06:15 PM–07:50 PM
Tu 06:15 PM–07:50 PM
We 06:15 PM–07:50 PM
Th 06:15 PM–07:50 PM

Instructor Points Enrollment Method of Instruction
Daniel Rabinowitz
3 Open for Enrollment
(auto-fill Wait List)
In-Person
STATISTICAL MACHINE LEARNING
STAT4241S001 3 points.
Prerequisites: STAT GU4206 The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.
Course Number Section/Call Number Session Times/Location
STAT4241S001 001/10132 Session A Mo 02:45 PM–04:20 PM
Tu 02:45 PM–04:20 PM
We 02:45 PM–04:20 PM
Th 02:45 PM–04:20 PM

Instructor Points Enrollment Method of Instruction
Banu Baydil
3 Open for Enrollment
(auto-fill Wait List)
In-Person
STATISTICAL METHODS FOR FINANCE
STAT4261S001 3 points.
Prerequisites: STAT GU4204 and STAT GU4205 A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.
Course Number Section/Call Number Session Times/Location
STAT4261S001 001/10133 Session A Tu 09:00 AM–12:10 PM
Th 09:00 AM–12:10 PM

Instructor Points Enrollment Method of Instruction
Hammou El Barmi
3 Open for Enrollment
(auto-fill Wait List)
In-Person
PROBABILITY
STAT5203W001 3 points.

Prerequisites: At least one semester of calculus. A calculus-based introduction to probability theory. Topics covered include random variables, conditional probability, expectation, independence, Bayes rule, important distributions, joint distributions, moment generating functions, central limit theorem, laws of large numbers and Markovs inequality.

Course Number Section/Call Number Session Times/Location
STAT5203W001 001/10134 Session A Mo 04:30 PM–06:05 PM
Tu 04:30 PM–06:05 PM
We 04:30 PM–06:05 PM
Th 04:30 PM–06:05 PM

Instructor Points Enrollment Method of Instruction
Young Kim
3 Open for Enrollment
(auto-fill Wait List)
In-Person
STATISTICAL INFERENCE
STAT5204W001 3 points.

Prerequisites: STAT GR5203 or the equivalent, and two semesters of calculus. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals, maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.

Course Number Section/Call Number Session Times/Location
STAT5204W001 001/10135 Session A Mo 06:15 PM–07:50 PM
Tu 06:15 PM–07:50 PM
We 06:15 PM–07:50 PM
Th 06:15 PM–07:50 PM

Instructor Points Enrollment Method of Instruction
David Rios
3 Open for Enrollment
(auto-fill Wait List)
In-Person
LINEAR REGRESSION MODELS
STAT5205W001 3 points.

Prerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.

Course Number Section/Call Number Session Times/Location
STAT5205W001 001/10440 Session A Mo 06:15 PM–07:50 PM
Tu 06:15 PM–07:50 PM
We 06:15 PM–07:50 PM
Th 06:15 PM–07:50 PM

Instructor Points Enrollment Method of Instruction
Daniel Rabinowitz
3 Open for Enrollment
(auto-fill Wait List)
In-Person
STAT COMP & INTRO DATA SCIENCE
STAT5206S001 3 points.
Prerequisites: STAT GU5204 and STAT GU5205 Open to MA students in Statistics only Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.
Course Number Section/Call Number Session Times/Location
STAT5206S001 001/10136 Session A Mo 04:30 PM–06:05 PM
Tu 04:30 PM–06:05 PM
We 04:30 PM–06:05 PM
Th 04:30 PM–06:05 PM

Instructor Points Enrollment Method of Instruction
Gabriel Young
3 Open for Enrollment
(auto-fill Wait List)
In-Person
Time Series Analysis
STAT5221S001 3 points.
Open to MA students in Statistics only Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.
Course Number Section/Call Number Session Times/Location
STAT5221S001 001/10137 Session A Mo 01:00 PM–04:10 PM
We 01:00 PM–04:10 PM

Instructor Points Enrollment Method of Instruction
Rongning Wu
3 Open for Enrollment
(auto-fill Wait List)
In-Person
BAYESIAN STATISTICS
STAT5224W001 3 points.

This course introduces the Bayesian paradigm for statistical inference.  Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models, Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.

 

Prerequisites: A course in the theory of statistical inference, such as STAT GU4204/GR5204 a  course in statistical modeling and data analysis such as STAT GU4205/GR5205.

Course Number Section/Call Number Session Times/Location
STAT5224W001 001/10138 Session B Mo 06:15 PM–07:50 PM
Tu 06:15 PM–07:50 PM
We 06:15 PM–07:50 PM
Th 06:15 PM–07:50 PM

Instructor Points Enrollment Method of Instruction
Daniel Rabinowitz
3 Open for Enrollment
(auto-fill Wait List)
In-Person
STATISTICAL MACHINE LEARNING
STAT5241S001 3 points.
Prerequisites: STAT GR5206 or the equivalent. Open to MA students in Statistics only The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.
Course Number Section/Call Number Session Times/Location
STAT5241S001 001/10139 Session A Mo 02:45 PM–04:20 PM
Tu 02:45 PM–04:20 PM
We 02:45 PM–04:20 PM
Th 02:45 PM–04:20 PM

Instructor Points Enrollment Method of Instruction
Banu Baydil
3 Open for Enrollment
(auto-fill Wait List)
In-Person
STATISTICAL METHODS FOR FINANCE
STAT5261S001 3 points.
Prerequisites: STAT GR5204 or the equivalent. STAT GR5205 is recommended. Open to MA students in Statistics only A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.
Course Number Section/Call Number Session Times/Location
STAT5261S001 001/10140 Session A Tu 09:00 AM–12:10 PM
Th 09:00 AM–12:10 PM

Instructor Points Enrollment Method of Instruction
Hammou El Barmi
3 Open for Enrollment
(auto-fill Wait List)
In-Person
TOPICS IN MODERN STATISTICS
STAT5293G001 3 points.

Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.

Course Number Section/Call Number Session Times/Location
STAT5293G001 001/12408 Session B
Instructor Points Enrollment Method of Instruction
Emanuel Ben-David
3 Open for Enrollment
(auto-fill Wait List)
On-Line Only
MA Mentored Research
STAT5398G002 1 points.

This course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.

Course Number Section/Call Number Session Times/Location
STAT5398G002 002/11262 X Summer Session
Instructor Points Enrollment Method of Instruction
Xiaofei Shi
1 Registration Block
(no Adds)
(self-man. Wait List)
On-Line Only
MA Mentored Research
STAT5398G003 1 points.

This course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.

Course Number Section/Call Number Session Times/Location
STAT5398G003 003/13284 R Summer Session
Instructor Points Enrollment Method of Instruction
Demissie Alemayehu
1 Registration Block
(no Adds)
(self-man. Wait List)
On-Line Only