# Statistics

The Department of Statistics offers courses in the basic principles and techniques of probability and statistics, advanced theory and methods courses, courses in stochastic processes and methods, and courses statistical methods in finance.

For questions about specific courses, contact the department.

For questions about specific courses, contact the department.

### Courses

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

STAT1001W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 10:10-11:25Th 10:10-11:25

##### Section/Call Number

001/13327##### Enrollment

59 of 75##### Instructor

Pratyay DattaTian Zheng

##### Course Number

STAT1001W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

002/13328##### Enrollment

69 of 75##### Instructor

Anthony DonoghueA 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

STAT1001W003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 08:40-09:55We 08:40-09:55

##### Section/Call Number

003/13329##### Enrollment

57 of 75##### Instructor

Musa Elbulok##### Course Number

STAT1101W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 08:40-09:55Th 08:40-09:55

##### Section/Call Number

001/13330##### Enrollment

82 of 86##### Instructor

Alexander Clark##### Course Number

STAT1101W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

002/13331##### Enrollment

69 of 86##### Instructor

Ha Nguyen##### Course Number

STAT1101W003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 08:40-09:55We 08:40-09:55

##### Section/Call Number

003/13332##### Enrollment

73 of 86##### Instructor

Dobrin Marchev##### Course Number

STAT1201W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 08:40-09:55We 08:40-09:55

##### Section/Call Number

001/13333##### Enrollment

81 of 86##### Instructor

Banu Baydil##### Course Number

STAT1201W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 08:40-09:55Th 08:40-09:55

##### Section/Call Number

002/13334##### Enrollment

76 of 86##### Instructor

David RiosPrerequisites: one semester of calculus. 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. Serves as the pre-requisite for ECON W3412.

##### Course Number

STAT1201W003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 14:40-15:55We 14:40-15:55

##### Section/Call Number

003/13335##### Enrollment

90 of 82##### Instructor

Chenyang Zhong##### Course Number

STAT1201W004##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

004/13336##### Enrollment

84 of 86##### Instructor

Banu Baydil##### Course Number

STAT1202W001##### Format

In-Person##### Points

1 pts#### Fall 2023

##### Times/Location

Fr 10:10-12:00##### Section/Call Number

001/13337##### Enrollment

10 of 25##### Instructor

Ronald Neath##### Course Number

STAT2102W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 16:10-17:25Th 16:10-17:25

##### Section/Call Number

001/13338##### Enrollment

49 of 86##### Instructor

Alex PijyanPrerequisites: An introductory course in statistics (STAT UN1101 is recommended). Students without programming experience in R might find STAT UN2102 very helpful. Develops critical thinking and data analysis skills for regression analysis in science and policy settings. Simple and multiple linear regression, non-linear and logistic models, random-effects models. Implementation in a statistical package. Emphasis on real-world examples and on planning, proposing, implementing, and reporting.

##### Course Number

STAT2103W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 14:40-15:55We 14:40-15:55

##### Section/Call Number

001/13339##### Enrollment

28 of 86##### Instructor

Wayne LeeThis is a course in intermediate statistical inference techniques in the context of applied research

questions in data science. Assuming some prior exposure to probability and statistics, this course will

first introduce the student to the principles of Bayesian inference, then apply them in estimation and

prediction in the context of linear and generalized linear models, counting and classification, mixture and

multilevel models, including scientific computation (like MCMC methods). Students will also learn

about the main benefits of using Bayesian vs. frequentist methods, like naturally combining prior

information with the data; posterior probabilities as easier to interpret alternatives to p-values; parameter

estimation “pooling” in hierarchical model and so on.

##### Course Number

STAT3104W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 14:40-15:55Th 14:40-15:55

##### Section/Call Number

001/13340##### Enrollment

18 of 50##### Instructor

Dobrin Marchev##### Course Number

STAT3105W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 14:40-15:55We 14:40-15:55

##### Section/Call Number

001/13341##### Enrollment

40 of 86##### Instructor

Alex Pijyan##### Course Number

STAT3107W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

001/13342##### Enrollment

2 of 5##### Instructor

Anne van Delft##### Course Number

STAT3107W002##### Format

In-Person##### Points

1 pts#### Fall 2023

##### Section/Call Number

002/20849##### Enrollment

2 of 2##### Instructor

Arian MalekiPrerequisites: 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

STAT3107W003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

003/21119##### Enrollment

1 of 1##### Instructor

David RiosPrerequisites: Calculus through multiple integration and infinite sums. A calculus-based tour of the fundamentals of probability theory and statistical inference. Probability models, random variables, useful distributions, conditioning, expectations, law of large numbers, central limit theorem, point and confidence interval estimation, hypothesis tests, linear regression. This course replaces SIEO 4150.

##### Course Number

STAT4001W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-20:40##### Section/Call Number

001/13343##### Enrollment

140 of 189##### Instructor

Isabella Sanders##### Course Number

STAT4203W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 10:10-11:25We 10:10-11:25

##### Section/Call Number

001/13344##### Enrollment

60 of 86##### Instructor

Richard Davis##### Course Number

STAT4203W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 18:10-19:25Th 18:10-19:25

##### Section/Call Number

002/13345##### Enrollment

29 of 86##### Instructor

David Rios##### Course Number

STAT4203W003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 18:10-19:25Th 18:10-19:25

##### Section/Call Number

003/13346##### Enrollment

38 of 40##### Instructor

David Rios##### Course Number

STAT4204W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

001/13347##### Enrollment

48 of 86##### Instructor

Cristian Pasarica##### Course Number

STAT4204W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

002/13348##### Enrollment

1 of 15##### Instructor

Cristian Pasarica##### Course Number

STAT4205W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 13:10-14:25We 13:10-14:25

##### Section/Call Number

001/13349##### Enrollment

11 of 86##### Instructor

Steven Campbell##### Course Number

STAT4205W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 14:40-15:55Th 14:40-15:55

##### Section/Call Number

002/13350##### Enrollment

16 of 25##### Instructor

Philip Protter##### Course Number

STAT4205W003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 19:40-20:55We 19:40-20:55

##### Section/Call Number

003/13351##### Enrollment

3 of 25##### Instructor

Jeonghoe Lee##### Course Number

STAT4205W004##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 08:40-09:55Th 08:40-09:55

##### Section/Call Number

004/13352##### Enrollment

21 of 25##### Instructor

Gabriel Young##### Course Number

STAT4205W005##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 08:40-09:55We 08:40-09:55

##### Section/Call Number

005/13353##### Enrollment

12 of 25##### Instructor

Yuqi Gu##### Course Number

STAT4206W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 10:10-12:40##### Section/Call Number

001/13354##### Enrollment

7 of 35##### Instructor

Wayne Lee##### Course Number

STAT4207W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 11:40-12:55We 11:40-12:55

##### Section/Call Number

001/13355##### Enrollment

24 of 35##### Instructor

Mark Brown##### Course Number

STAT4221W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 18:10-19:25Th 18:10-19:25

##### Section/Call Number

001/13356##### Enrollment

12 of 35##### Instructor

Rongning Wu##### Course Number

STAT4224W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

001/13357##### Enrollment

23 of 35##### Instructor

Ronald NeathPrerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will be covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.

##### Course Number

STAT4243W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

We 18:10-20:55##### Section/Call Number

001/13358##### Enrollment

15 of 25##### Instructor

Ying LiuTian Zheng

##### Course Number

STAT4261W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 10:10-12:40##### Section/Call Number

001/13359##### Enrollment

7 of 25##### Instructor

Hammou El Barmi##### Course Number

STAT4263G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 18:10-19:25Th 18:10-19:25

##### Section/Call Number

001/13360##### Enrollment

5 of 35##### Instructor

Yisha Yao##### Course Number

STAT4263G002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Sa 10:10-12:40##### Section/Call Number

002/13361##### Enrollment

5 of 35##### Instructor

Franz Rembart##### Course Number

STAT4264G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 16:10-17:25We 16:10-17:25

##### Section/Call Number

001/13362##### Enrollment

10 of 35##### Instructor

Graeme Baker##### Course Number

STAT4264G002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

002/13363##### Enrollment

13 of 35##### Instructor

Lars Nielsen##### Course Number

STAT4291W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 17:10-19:40##### Section/Call Number

001/13364##### Enrollment

6 of 25##### Instructor

Demissie AlemayehuPrerequisites: 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

STAT5204W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

001/13370##### Enrollment

7 of 35##### Instructor

Cristian Pasarica##### Course Number

STAT5205W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 14:40-15:55Th 14:40-15:55

##### Section/Call Number

001/13381##### Enrollment

94 of 86##### Instructor

Philip ProtterPrerequisites: 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

STAT5205W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 19:40-20:55We 19:40-20:55

##### Section/Call Number

002/13382##### Enrollment

81 of 86##### Instructor

Jeonghoe LeePrerequisites: 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

STAT5205W003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 08:40-09:55Th 08:40-09:55

##### Section/Call Number

003/13383##### Enrollment

75 of 86##### Instructor

Gabriel YoungPrerequisites: 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

STAT5205W004##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 08:40-09:55We 08:40-09:55

##### Section/Call Number

004/13384##### Enrollment

30 of 86##### Instructor

Yuqi Gu##### Course Number

STAT5206W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 10:10-12:40##### Section/Call Number

001/13385##### Enrollment

80 of 125##### Instructor

Wayne Lee##### Course Number

STAT5206W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 10:10-12:40##### Section/Call Number

002/13386##### Enrollment

123 of 125##### Instructor

Yongchan Kwon##### Course Number

STAT5206W003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Th 18:10-20:40##### Section/Call Number

003/13387##### Enrollment

66 of 125##### Instructor

Haiyuan Wang##### Course Number

STAT5207W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 11:40-12:55We 11:40-12:55

##### Section/Call Number

001/13388##### Enrollment

24 of 86##### Instructor

Mark Brown##### Course Number

STAT5221W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 18:10-19:25Th 18:10-19:25

##### Section/Call Number

001/13389##### Enrollment

57 of 86##### Instructor

Rongning Wu##### Course Number

STAT5224W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

001/13390##### Enrollment

17 of 86##### Instructor

Ronald Neath##### Course Number

STAT5242W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 10:10-12:40##### Section/Call Number

001/13391##### Enrollment

84 of 86##### Instructor

Samory KpotufeKamiar Rahnama Rad

##### Course Number

STAT5242W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

We 10:10-12:40##### Section/Call Number

002/13392##### Enrollment

70 of 86##### Instructor

Kamiar Rahnama RadSamory Kpotufe

##### Course Number

STAT5242W003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Th 16:10-18:40##### Section/Call Number

003/15627##### Enrollment

20 of 86##### Instructor

Parijat DubePrerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.

##### Course Number

STAT5243W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

We 18:10-20:55##### Section/Call Number

001/13393##### Enrollment

33 of 50##### Instructor

Ying LiuTian Zheng

This course is an optional companion lab course for GR5242 Advanced Machine Learning. The aim of this course is to help students acquire the basic computational skills in Tensorflow and Python to implement machine learning models. Lab class materials will be aligned closely with the topics covered in GR5242. Google Colab and Jupyter notebooks will be used as the main tools for the hands-on lab exercises.

Open to GR5242 students only.

**Prerequisites**

Some familiarity with python is assumed, but we will begin the class with a tutorial on 'Python for machine learning'.

##### Course Number

STAT5245G001##### Format

In-Person##### Points

1 pts#### Fall 2023

##### Times/Location

Fr 17:35-18:35##### Section/Call Number

001/13990##### Enrollment

42 of 86##### Instructor

Ka-Yi NgThis course is an optional companion lab course for GR5242 Advanced Machine Learning. The aim of this course is to help students acquire the basic computational skills in Tensorflow and Python to implement machine learning models. Lab class materials will be aligned closely with the topics covered in GR5242. Google Colab and Jupyter notebooks will be used as the main tools for the hands-on lab exercises.

Open to GR5242 students only.

**Prerequisites**

Some familiarity with python is assumed, but we will begin the class with a tutorial on 'Python for machine learning'.

##### Course Number

STAT5245G002##### Format

In-Person##### Points

1 pts#### Fall 2023

##### Times/Location

Fr 18:45-19:45##### Section/Call Number

002/13991##### Enrollment

0 of 86##### Instructor

Ka-Yi Ng##### Course Number

STAT5261W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 10:10-12:40##### Section/Call Number

001/13396##### Enrollment

66 of 125##### Instructor

Hammou El BarmiPrerequisites: STAT GR5205 or the equivalent. Available to SSP, SMP Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering.

##### Course Number

STAT5263G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 18:10-19:25Th 18:10-19:25

##### Section/Call Number

001/13397##### Enrollment

55 of 100##### Instructor

Yisha YaoPrerequisites: STAT GR5205 or the equivalent. Available to SSP, SMP Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering.

##### Course Number

STAT5263G002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Sa 10:10-12:40##### Section/Call Number

002/13398##### Enrollment

62 of 100##### Instructor

Franz Rembart##### Course Number

STAT5264G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 16:10-17:25We 16:10-17:25

##### Section/Call Number

001/13399##### Enrollment

39 of 65##### Instructor

Graeme Baker##### Course Number

STAT5264G002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-19:25We 18:10-19:25

##### Section/Call Number

002/13400##### Enrollment

80 of 100##### Instructor

Lars Nielsen##### Course Number

STAT5291W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 17:10-19:40##### Section/Call Number

001/13401##### Enrollment

55 of 325##### Instructor

Demissie AlemayehuTopics 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

STAT5293G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 19:00-21:30##### Section/Call Number

001/13403##### Enrollment

13 of 86##### Instructor

Ori ShentalTopics 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

STAT5293G002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 13:10-15:40##### Section/Call Number

002/13402##### Enrollment

50 of 66##### Instructor

Xiaofu HeTopics 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

STAT5293GOO4##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 18:10-20:40##### Section/Call Number

OO4/15225##### Enrollment

54 of 50##### Instructor

Lei KangThe course aims to teach MA in Statistics students how to manage their careers and develop professionally. Topics include resume and cover-letter writing, negotiation, mentoring, interviewing skills and communication across global teams. Top professionals from across the globe speak to students and help improve leadership skills.

##### Course Number

STAT5391G003##### Format

In-Person##### Points

0 pts#### Fall 2023

##### Times/Location

We 16:10-17:25##### Section/Call Number

003/13608##### Enrollment

36 of 40##### Instructor

Banu BaydilThe course aims to teach MA in Statistics students how to manage their careers and develop professionally. Topics include resume and cover-letter writing, negotiation, mentoring, interviewing skills and communication across global teams. Top professionals from across the globe speak to students and help improve leadership skills.

##### Course Number

STAT5391G005##### Format

In-Person##### Points

0 pts#### Fall 2023

##### Times/Location

Mo 11:40-12:55We 11:40-12:55

##### Section/Call Number

005/##### Enrollment

0 of 25##### Instructor

Banu BaydilThis 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

STAT5398G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

001/13404##### Enrollment

27 of 25##### Instructor

Demissie AlemayehuThis 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

STAT5398G002##### Format

In-Person##### Points

3 ptsThis 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

STAT5398G003##### Format

On-Line Only##### Points

3 pts##### Course Number

STAT5398G004##### Format

In-Person##### Points

3 pts##### Course Number

STAT5399G001##### Format

In-Person##### Points

1 pts#### Fall 2023

##### Section/Call Number

001/13405##### Enrollment

16 of 25##### Instructor

Demissie AlemayehuPrerequisites: Calculus This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.

##### Course Number

STAT5701W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 17:30-20:00##### Section/Call Number

001/13406##### Enrollment

105 of 120##### Instructor

Tat Sang FungPrerequisites: Calculus This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.

##### Course Number

STAT5701W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 19:40-20:55Th 19:40-20:55

##### Section/Call Number

002/13407##### Enrollment

84 of 120##### Instructor

Alberto Gonzalez SanzPrerequisites: programming. This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.

##### Course Number

STAT5702W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 16:10-17:25We 16:10-17:25

##### Section/Call Number

001/13408##### Enrollment

83 of 86##### Instructor

Joyce RobbinsPrerequisites: programming. This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.

##### Course Number

STAT5702W002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 16:10-17:25Th 16:10-17:25

##### Section/Call Number

002/13409##### Enrollment

79 of 86##### Instructor

Joyce Robbins##### Course Number

STAT5703W001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 18:10-19:25Th 18:10-19:25

##### Section/Call Number

001/13410##### Enrollment

32 of 50##### Instructor

Dobrin Marchev##### Course Number

STAT6101G001##### Format

In-Person##### Points

4 pts#### Fall 2023

##### Times/Location

Mo 10:10-11:25We 10:10-11:25

##### Section/Call Number

001/13411##### Enrollment

15 of 25##### Instructor

Ming Yuan##### Course Number

STAT6103G001##### Format

In-Person##### Points

4 pts#### Fall 2023

##### Times/Location

Tu 10:10-11:25Th 10:10-11:25

##### Section/Call Number

001/13412##### Enrollment

11 of 25##### Instructor

Bianca DumitrascuPrerequisites: STAT GR6102 or instructor permission. The Deparatments doctoral student consulting practicum. Students undertake pro bono consulting activities for Columbia community researchers under the tutelage of a faculty mentor.

##### Course Number

STAT6105G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 12:00-13:15##### Section/Call Number

001/13413##### Enrollment

4 of 15##### Instructor

Regina Dolgoarshinnykh##### Course Number

STAT6106G001##### Format

In-Person##### Points

4 pts#### Fall 2023

##### Times/Location

Mo 08:40-09:55We 08:40-09:55

##### Section/Call Number

001/13414##### Enrollment

8 of 50##### Instructor

Andrew Gelman##### Course Number

STAT6201G001##### Format

In-Person##### Points

4 pts#### Fall 2023

##### Times/Location

Tu 14:40-15:55Th 14:40-15:55

##### Section/Call Number

001/13415##### Enrollment

16 of 25##### Instructor

Cynthia Rush##### Course Number

STAT6203G001##### Format

In-Person##### Points

4 pts#### Fall 2023

##### Times/Location

Th 18:10-20:40##### Section/Call Number

001/13416##### Enrollment

6 of 25##### Instructor

Anne van Delft##### Course Number

STAT6301G001##### Format

In-Person##### Points

4 pts#### Fall 2023

##### Times/Location

Mo 14:40-15:55We 14:40-15:55

##### Section/Call Number

001/13417##### Enrollment

17 of 25##### Instructor

Sumit Mukherjee##### Course Number

STAT6303G001##### Format

In-Person##### Points

4 pts#### Fall 2023

##### Times/Location

Tu 10:10-11:25Th 10:10-11:25

##### Section/Call Number

001/13418##### Enrollment

12 of 25##### Instructor

Marcel NutzIndependent Study with Faculty Advisor must be registered for every semester after first academic year

##### Course Number

STAT8001R001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

001/20907##### Enrollment

0 of 5##### Instructor

Marco Avella MedinaIndependent Study with Faculty Advisor must be registered for every semester after first academic year

##### Course Number

STAT8001R002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

002/20908##### Enrollment

2 of 5##### Instructor

David BleiIndependent Study with Faculty Advisor must be registered for every semester after first academic year

##### Course Number

STAT8001R003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

003/20909##### Enrollment

0 of 5##### Instructor

John Cunningham##### Course Number

STAT8001R004##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

004/20910##### Enrollment

2 of 5##### Instructor

Richard Davis##### Course Number

STAT8001R005##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

005/20911##### Enrollment

0 of 5##### Instructor

Victor de la Pena##### Course Number

STAT8001R006##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

006/20912##### Enrollment

1 of 5##### Instructor

Bianca Dumitrascu##### Course Number

STAT8001R007##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

007/20913##### Enrollment

3 of 5##### Instructor

Andrew Gelman##### Course Number

STAT8001R008##### Format

In-Person##### Points

3 pts##### Course Number

STAT8001R009##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

009/20931##### Enrollment

2 of 5##### Instructor

Ioannis Karatzas##### Course Number

STAT8001R010##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

010/20932##### Enrollment

2 of 5##### Instructor

Samory Kpotufe##### Course Number

STAT8001R011##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

011/20933##### Enrollment

1 of 5##### Instructor

Jingchen Liu##### Course Number

STAT8001R012##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

012/20934##### Enrollment

0 of 5##### Instructor

Shaw-Hwa Lo##### Course Number

STAT8001R013##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

013/20936##### Enrollment

4 of 5##### Instructor

Arian Maleki##### Course Number

STAT8001R014##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

014/20937##### Enrollment

3 of 5##### Instructor

Sumit Mukherjee##### Course Number

STAT8001R015##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

015/20938##### Enrollment

1 of 5##### Instructor

Marcel Nutz##### Course Number

STAT8001R016##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

016/20939##### Enrollment

2 of 5##### Instructor

Liam Paninski##### Course Number

STAT8001R017##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

017/20943##### Enrollment

1 of 5##### Instructor

Philip Protter##### Course Number

STAT8001R018##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

018/20947##### Enrollment

0 of 5##### Instructor

Daniel Rabinowitz##### Course Number

STAT8001R019##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

019/20950##### Enrollment

1 of 5##### Instructor

Cynthia Rush##### Course Number

STAT8001R020##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

020/20952##### Enrollment

1 of 5##### Instructor

Bodhisattva Sen##### Course Number

STAT8001R021##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

021/20958##### Enrollment

1 of 5##### Instructor

Michael Sobel##### Course Number

STAT8001R022##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

022/20959##### Enrollment

0 of 5##### Instructor

Simon Tavare##### Course Number

STAT8001R023##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

023/20960##### Enrollment

0 of 5##### Instructor

Anne van Delft##### Course Number

STAT8001R024##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

024/20961##### Enrollment

4 of 5##### Instructor

Zhiliang Ying##### Course Number

STAT8001R025##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

025/20962##### Enrollment

2 of 5##### Instructor

Ming Yuan##### Course Number

STAT8001R026##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Section/Call Number

026/20963##### Enrollment

3 of 5##### Instructor

Tian Zheng.

##### Course Number

STAT8101G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Th 10:00-12:00##### Section/Call Number

001/13420##### Enrollment

9 of 25##### Instructor

John Cunningham##### Course Number

STAT8201G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 13:00-14:30##### Section/Call Number

001/13421##### Enrollment

7 of 30##### Instructor

Liam Paninski##### Course Number

STAT8201G002##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Tu 14:30-16:30##### Section/Call Number

002/13422##### Enrollment

8 of 25##### Instructor

Marco Avella Medina.

##### Course Number

STAT8201G003##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

We 14:00-16:00##### Section/Call Number

003/13424##### Enrollment

8 of 25##### Instructor

Emmanuel Abbe##### Course Number

STAT9201G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Mo 16:10-17:25##### Section/Call Number

001/13425##### Enrollment

38 of 45##### Instructor

Anne van DelftYuqi Gu

##### Course Number

STAT9301G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Fr 11:40-12:55##### Section/Call Number

001/13426##### Enrollment

2 of 25##### Instructor

Ivan Corwin##### Course Number

STAT9302G001##### Format

In-Person##### Points

1 pts#### Fall 2023

##### Times/Location

Th 13:10-14:25##### Section/Call Number

001/13427##### Enrollment

4 of 25##### Instructor

Chenyang ZhongSumit Mukherjee

##### Course Number

STAT9303G001##### Format

In-Person##### Points

3 pts#### Fall 2023

##### Times/Location

Th 16:10-17:25##### Section/Call Number

001/13428##### Enrollment

10 of 21##### Instructor

Marcel NutzPhilip Protter