# 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

##### Course Number

STAT1001W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13776##### Enrollment

105 of 110##### Instructor

Guy Cohen##### Course Number

STAT1001W002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

002/13777##### Enrollment

54 of 86##### Instructor

Musa ElbulokA 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 2022

##### Times/Location

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

##### Section/Call Number

003/16024##### Enrollment

10 of 86##### Instructor

Shaw-Hwa Lo##### Course Number

STAT1101W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13778##### Enrollment

65 of 86##### Instructor

Alexander Clark##### Course Number

STAT1101W002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

002/13779##### Enrollment

79 of 86##### Instructor

Alex Pijyan##### Course Number

STAT1101W003##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

003/13780##### Enrollment

48 of 86##### Instructor

David Rios##### Course Number

STAT1201W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13781##### Enrollment

85 of 86##### Instructor

Joyce Robbins##### Course Number

STAT1201W002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

002/13782##### Enrollment

67 of 86##### Instructor

Johannes Wiesel##### Course Number

STAT1201W003##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

003/13783##### Enrollment

75 of 86##### Instructor

Dobrin Marchev##### Course Number

STAT1201W004##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

004/13784##### Enrollment

64 of 86##### Instructor

Chenyang Zhong##### Course Number

STAT1202W001##### Format

In-Person##### Points

1 pts#### Fall 2022

##### Times/Location

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

001/13785##### Enrollment

20 of 25##### Instructor

Ronald Neath##### Course Number

STAT2102W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13786##### Enrollment

66 of 120##### Instructor

Anthony DonoghuePrerequisites: 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 2022

##### Times/Location

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

##### Section/Call Number

001/13787##### Enrollment

23 of 86##### Instructor

Wayne Lee##### Course Number

STAT3105W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

Tu 11:40-12:55Th 11:40-12:55

##### Section/Call Number

001/13788##### Enrollment

37 of 86##### Instructor

David Rios##### Course Number

STAT3107W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Section/Call Number

001/13789##### Enrollment

2 of 1##### Instructor

Ronald Neath##### Course Number

STAT3107W002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Section/Call Number

002/13790##### Enrollment

1 of 5##### Instructor

Tian ZhengPrerequisites: 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 2022

##### Section/Call Number

003/20448##### Enrollment

0 of 5##### Instructor

Johannes WieselPrerequisites: 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 2022

##### Times/Location

Tu 13:10-14:25Th 13:10-14:25

##### Section/Call Number

001/13791##### Enrollment

113 of 152##### Instructor

Banu Baydil##### Course Number

STAT4203W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13792##### Enrollment

50 of 86##### Instructor

Carsten Chong##### Course Number

STAT4203W002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

002/13793##### Enrollment

37 of 86##### Instructor

Cristian Pasarica##### Course Number

STAT4203W003##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

003/13794##### Enrollment

17 of 35##### Instructor

Cristian Pasarica##### Course Number

STAT4204W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13795##### Enrollment

31 of 86##### Instructor

Hammou El Barmi##### Course Number

STAT4204W002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

002/13796##### Enrollment

5 of 15##### Instructor

Hammou El Barmi##### Course Number

STAT4205W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13797##### Enrollment

26 of 86##### Instructor

Ronald Neath##### Course Number

STAT4205W002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

002/13798##### Enrollment

10 of 15##### Instructor

Philip Protter##### Course Number

STAT4205W003##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

003/13799##### Enrollment

11 of 11##### Instructor

Banu Baydil##### Course Number

STAT4205W004##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

004/13800##### Enrollment

4 of 25##### Instructor

Alex Pijyan##### Course Number

STAT4205W005##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

005/13801##### Enrollment

8 of 25##### Instructor

Yuqi Gu##### Course Number

STAT4206W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/14194##### Enrollment

5 of 10##### Instructor

Wayne Lee##### Course Number

STAT4207W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13802##### Enrollment

27 of 45##### Instructor

Richard Davis##### Course Number

STAT4221W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13803##### Enrollment

11 of 35##### Instructor

Rongning Wu##### Course Number

STAT4224W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13804##### Enrollment

22 of 35##### Instructor

Ronald Neath##### Course Number

STAT4243W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13805##### Enrollment

15 of 19##### Instructor

Ying Liu##### Course Number

STAT4261W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13806##### Enrollment

12 of 25##### Instructor

Zhiliang Ying##### Course Number

STAT4263G001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13808##### Enrollment

2 of 35##### Instructor

Gokce Dayanikli##### Course Number

STAT4263G002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

002/13809##### Enrollment

0 of 35##### Instructor

Franz Rembart##### Course Number

STAT4264G001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13810##### Enrollment

9 of 35##### Instructor

Ioannis Karatzas##### Course Number

STAT4264G002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

002/13811##### Enrollment

5 of 35##### Instructor

Lars Nielsen##### Course Number

STAT4291W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13812##### Enrollment

8 of 25##### Instructor

Demissie Alemayehu##### Course Number

STAT5203W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13814##### Enrollment

16 of 25##### Instructor

Cristian Pasarica##### Course Number

STAT5204W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13820##### Enrollment

10 of 21##### Instructor

Hammou El Barmi##### Course Number

STAT5205W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13826##### Enrollment

43 of 120##### 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 2022

##### Times/Location

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

##### Section/Call Number

002/13827##### Enrollment

115 of 115##### Instructor

Banu BaydilPrerequisites: 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 2022

##### Times/Location

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

##### Section/Call Number

003/13828##### Enrollment

83 of 86##### Instructor

Alex PijyanPrerequisites: 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 2022

##### Times/Location

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

##### Section/Call Number

004/13829##### Enrollment

77 of 86##### Instructor

Yuqi Gu##### Course Number

STAT5206W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13833##### Enrollment

130 of 155##### Instructor

Wayne Lee##### Course Number

STAT5206W002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

002/13834##### Enrollment

114 of 126##### Instructor

Yongchan Kwon##### Course Number

STAT5206W003##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

003/13835##### Enrollment

70 of 125##### Instructor

Haiyuan Wang##### Course Number

STAT5207W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13836##### Enrollment

30 of 86##### Instructor

Richard Davis##### Course Number

STAT5221W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13837##### Enrollment

60 of 86##### Instructor

Rongning Wu##### Course Number

STAT5224W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13838##### Enrollment

31 of 86##### Instructor

Ronald Neath##### Course Number

STAT5242W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13839##### Enrollment

95 of 125##### Instructor

John CunninghamKamiar Rahnama Rad

##### Course Number

STAT5242W002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

Tu 11:40-12:55Th 11:40-12:55

##### Section/Call Number

002/13840##### Enrollment

75 of 125##### Instructor

Kamiar Rahnama RadJohn Cunningham

##### Course Number

STAT5243W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13841##### Enrollment

33 of 56##### Instructor

Ying LiuThis 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 2022

##### Times/Location

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

001/13843##### Enrollment

56 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 2022

##### Times/Location

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

002/13844##### Enrollment

26 of 86##### Instructor

Ka-Yi Ng##### Course Number

STAT5261W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13845##### Enrollment

61 of 135##### Instructor

Zhiliang YingPrerequisites: 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 2022

##### Times/Location

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

##### Section/Call Number

001/13846##### Enrollment

88 of 110##### Instructor

Gokce DayanikliPrerequisites: 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 2022

##### Times/Location

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

002/13847##### Enrollment

35 of 100##### Instructor

Franz Rembart##### Course Number

STAT5264G001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13848##### Enrollment

28 of 100##### Instructor

Ioannis Karatzas##### Course Number

STAT5264G002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

002/13849##### Enrollment

96 of 100##### Instructor

Lars Nielsen##### Course Number

STAT5291W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13850##### Enrollment

66 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 2022

##### Times/Location

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

001/13852##### Enrollment

8 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 2022

##### Times/Location

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

002/13851##### Enrollment

16 of 60##### 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

STAT5293G003##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

003/19994##### Enrollment

12 of 20##### Instructor

Mark HansenThe 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 2022

##### 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

1 pts#### Fall 2022

##### Section/Call Number

001/13853##### Enrollment

14 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

1 pts#### Fall 2022

##### Section/Call Number

002/14536##### Enrollment

1 of 3##### Instructor

Yongchan KwonThis 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

1 pts#### Fall 2022

##### Section/Call Number

003/18286##### Enrollment

2 of 1##### Instructor

Tian Zheng##### Course Number

STAT5398G004##### Format

In-Person##### Points

1 pts#### Fall 2022

##### Section/Call Number

004/19912##### Enrollment

3 of 10##### Instructor

David Rios##### Course Number

STAT5399G001##### Format

In-Person##### Points

1 pts#### Fall 2022

##### Section/Call Number

001/13854##### Enrollment

22 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 2022

##### Times/Location

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

##### Section/Call Number

001/13855##### Enrollment

192 of 210##### Instructor

Tat Sang FungPrerequisites: 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 2022

##### Times/Location

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

##### Section/Call Number

001/13856##### Enrollment

84 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 2022

##### Times/Location

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

##### Section/Call Number

002/13857##### Enrollment

102 of 95##### 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

STAT5702WV01##### Format

On-Line Only##### Points

3 pts#### Fall 2022

##### Section/Call Number

V01/19997##### Enrollment

3 of 99##### Instructor

Joyce Robbins##### Course Number

STAT5703W001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13858##### Enrollment

25 of 50##### Instructor

Dobrin Marchev##### Course Number

STAT6101G001##### Format

In-Person##### Points

4 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13859##### Enrollment

13 of 25##### Instructor

Ming Yuan##### Course Number

STAT6103G001##### Format

In-Person##### Points

4 pts#### Fall 2022

##### Times/Location

We 13:30-15:30##### Section/Call Number

001/13863##### Enrollment

3 of 25##### Instructor

Liam Paninski##### Course Number

STAT6105G001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13860##### Enrollment

8 of 15##### Instructor

Regina Dolgoarshinnykh##### Course Number

STAT6106G001##### Format

In-Person##### Points

4 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/18459##### Enrollment

1 of 50##### Instructor

Mark Hansen##### Course Number

STAT6201G001##### Format

In-Person##### Points

4 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13861##### Enrollment

17 of 25##### Instructor

Cynthia Rush##### Course Number

STAT6203G001##### Format

In-Person##### Points

4 pts#### Fall 2022

##### Times/Location

Th 11:30-13:30##### Section/Call Number

001/13862##### Enrollment

13 of 25##### Instructor

Arian Maleki##### Course Number

STAT6301G001##### Format

In-Person##### Points

4 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13864##### Enrollment

22 of 25##### Instructor

Marcel Nutz##### Course Number

STAT6303G001##### Format

In-Person##### Points

4 pts#### Fall 2022

##### Times/Location

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

##### Section/Call Number

001/13865##### Enrollment

7 of 25##### Instructor

Sumit Mukherjee.

##### Course Number

STAT8101G001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

Th 15:00-17:00##### Section/Call Number

001/18833##### Enrollment

13 of 20##### Instructor

Tian ZhengGregory Elsaesser

##### Course Number

STAT8201G001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

We 13:30-15:30##### Section/Call Number

001/13866##### Enrollment

5 of 30##### Instructor

Liam Paninski##### Course Number

STAT8201G002##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

002/13867##### Enrollment

13 of 25##### Instructor

Arian Maleki##### Course Number

STAT9201G001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13868##### Enrollment

40 of 45##### Instructor

Marco Avella MedinaAnne van Delft

Yuqi Gu

##### Course Number

STAT9301G001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13869##### Enrollment

4 of 25##### Instructor

Ivan Corwin##### Course Number

STAT9302G001##### Format

In-Person##### Points

1 pts#### Fall 2022

##### Times/Location

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

001/13870##### Enrollment

2 of 25##### Instructor

Sumit MukherjeeVictor de la Pena

##### Course Number

STAT9303G001##### Format

In-Person##### Points

3 pts#### Fall 2022

##### Times/Location

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

001/13871##### Enrollment

8 of 25##### Instructor

Marcel NutzPhilip Protter