# 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#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/13986##### Enrollment

0 of 86##### Instructor

Victor de la PenaA 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

STAT1001W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/13987##### Enrollment

0 of 86##### 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#### Spring 2025

##### Times/Location

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

##### Section/Call Number

003/13988##### Enrollment

0 of 86##### Instructor

Ashley Datta##### Course Number

STAT1101W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/13989##### Enrollment

0 of 160##### Instructor

Wayne Lee##### Course Number

STAT1101W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/13991##### Enrollment

0 of 86##### Instructor

Banu BaydilPrerequisites: 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

STAT1201W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/13992##### Enrollment

0 of 86##### Instructor

Hammou El Barmi##### Course Number

STAT1201W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/13993##### Enrollment

0 of 85##### Instructor

Joyce Robbins##### Course Number

STAT1201W003##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

003/13994##### Enrollment

0 of 86##### Instructor

Joyce RobbinsPrerequisites: 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

STAT1201W004##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

004/13995##### Enrollment

0 of 86##### Course Number

STAT2102W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/13996##### Enrollment

0 of 120##### 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#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/13998##### Enrollment

0 of 85##### Instructor

Daniel Rabinowitz##### Course Number

STAT2104W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/13999##### Enrollment

0 of 86##### Instructor

Ronald NeathPrerequisites: STAT UN2103. Students without programming experience in R might find STAT UN2102 very helpful. This course is a machine learning class from an application perspective. We will cover topics including data-based prediction, classification, specific classification methods (such as logistic regression and random forests), and basics of neural networks. Programming in homeworks will require R.

##### Course Number

STAT3106W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14000##### Enrollment

0 of 86##### Instructor

Wayne LeePrerequisites: 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

STAT3107W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Section/Call Number

001/14001##### Enrollment

0 of 2##### Instructor

Ronald NeathTopics in Modern Statistics that provide undergraduate 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. Courses listed are reviewed and approved by the Undergraduate Advisory Committee of the Department of Statistics. A good working knowledge of basic statistical concepts (likelihood,

Bayes' rule, Poisson processes, Markov chains, Gaussian random vectors), including especially linear-algebraic concepts related to regression and principal components analysis, is necessary. No previous experience with neural data is required.

##### Course Number

STAT3293W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14074##### Enrollment

0 of 16##### Instructor

Joyce RobbinsPrerequisites: 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#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14003##### Enrollment

0 of 100##### Instructor

Cristian PasaricaPrerequisites: 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

STAT4001W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/14004##### Enrollment

0 of 86##### Instructor

Sumit Mukherjee##### Course Number

STAT4203W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14011##### Enrollment

0 of 60##### Instructor

Marco Avella Medina##### Course Number

STAT4203W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/14010##### Enrollment

0 of 5##### Instructor

Marco Avella Medina##### Course Number

STAT4204W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14012##### Enrollment

0 of 45##### Instructor

Banu Baydil##### Course Number

STAT4204W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/14013##### Enrollment

0 of 35##### Instructor

Ashley Datta##### Course Number

STAT4205W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14014##### Enrollment

0 of 35##### Instructor

Ronald Neath##### Course Number

STAT4206W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14015##### Enrollment

0 of 40##### Instructor

Yongchan Kwon##### Course Number

STAT4207W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14016##### Enrollment

0 of 50##### Instructor

Anne van Delft##### Course Number

STAT4207W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/14017##### Enrollment

0 of 35##### Instructor

Mark Brown##### Course Number

STAT4221W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14018##### Enrollment

0 of 25##### Instructor

Franz RembartPrerequisites: STAT GU4204 or the equivalent. Statistical inference without parametric model assumption. Hypothesis testing using ranks, permutations, and order statistics. Nonparametric analogs of analysis of variance. Non-parametric regression, smoothing and model selection.

##### Course Number

STAT4222W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14019##### Enrollment

0 of 25##### Instructor

Arian MalekiThis 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

STAT4224W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14020##### Enrollment

0 of 25##### Instructor

Dobrin Marchev##### Course Number

STAT4234W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14021##### Enrollment

0 of 25##### Instructor

Rongning Wu##### Course Number

STAT4241W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14024##### Enrollment

0 of 50##### Instructor

Samory KpotufePrerequisites: 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#### Spring 2025

##### Times/Location

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

001/14028##### Enrollment

0 of 25##### Instructor

Alex PijyanPrerequisites: 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

STAT4243W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

002/14029##### Enrollment

0 of 25##### Instructor

Haiyuan WangPrerequisites: 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

STAT4243W003##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

003/14030##### Enrollment

0 of 30##### Instructor

Galen McKinley##### Course Number

STAT4261W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14031##### Enrollment

0 of 25##### Instructor

Zhiliang Ying##### Course Number

STAT4264G001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14032##### Enrollment

0 of 25##### Instructor

Steven Campbell##### Course Number

STAT4265G001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14033##### Enrollment

0 of 25##### Instructor

Graeme Baker##### Course Number

STAT4291W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14034##### Enrollment

0 of 25##### Instructor

Gabriel YoungPrerequisites: 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

STAT5203W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/14035##### Enrollment

0 of 35##### Instructor

Marco Avella MedinaPrerequisites: 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

STAT5205W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14038##### Enrollment

0 of 50##### Instructor

Ronald Neath##### Course Number

STAT5206W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14039##### Enrollment

0 of 50##### Instructor

Yongchan Kwon##### Course Number

STAT5207W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14040##### Enrollment

0 of 100##### Instructor

Mark Brown##### Course Number

STAT5221W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14041##### Enrollment

0 of 125##### Instructor

Franz RembartPrerequisites: STAT GR5205 Statistical inference without parametric model assumption. Hypothesis testing using ranks, permutations, and order statistics. Nonparametric analogs of analysis of variance. Non-parametric regression, smoothing and model selection.

##### Course Number

STAT5222W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14042##### Enrollment

0 of 86##### Instructor

Arian Maleki##### Course Number

STAT5224W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14043##### Enrollment

0 of 125##### Instructor

Dobrin Marchev##### Course Number

STAT5234W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14044##### Enrollment

0 of 86##### Instructor

Rongning Wu##### Course Number

STAT5241W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14045##### Enrollment

0 of 86##### Instructor

Genevera Allen##### Course Number

STAT5241W002##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/14046##### Enrollment

0 of 86##### Instructor

Yisha Yao##### Course Number

STAT5241W003##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

003/14047##### Enrollment

0 of 86##### Instructor

Alberto Gonzalez Sanz##### Course Number

STAT5241W004##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

004/14048##### Enrollment

0 of 86##### Instructor

Chenyang ZhongPrerequisites: 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#### Spring 2025

##### Times/Location

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

001/14049##### Enrollment

0 of 86##### Instructor

Alex PijyanPrerequisites: 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

STAT5243W002##### Points

3 pts#### Spring 2025

##### Times/Location

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

002/14050##### Enrollment

0 of 86##### Instructor

Haiyuan WangPrerequisites: 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

STAT5243W003##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

003/14051##### Enrollment

0 of 30##### Instructor

Galen McKinleyTian Zheng

##### Course Number

STAT5261W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14052##### Enrollment

0 of 150##### Instructor

Zhiliang Ying##### Course Number

STAT5264G001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14053##### Enrollment

0 of 86##### Instructor

Steven Campbell##### Course Number

STAT5265G001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14054##### Enrollment

0 of 135##### Instructor

Graeme Baker##### Course Number

STAT5291W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14055##### Enrollment

0 of 225##### Instructor

Gabriel YoungTopics 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#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14056##### Enrollment

0 of 86##### Instructor

Joyce RobbinsTopics 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#### Spring 2025

##### Times/Location

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

##### Section/Call Number

002/14057##### Enrollment

0 of 35##### Instructor

Philip ProtterTopics 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#### Spring 2025

##### Times/Location

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

##### Section/Call Number

003/14058##### Enrollment

0 of 86##### Instructor

Parijat Dube##### Course Number

STAT5293G004##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

004/14059##### Enrollment

0 of 86##### Instructor

Lei Kang##### Course Number

STAT5293G005##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

005/14060##### Enrollment

0 of 20##### Instructor

Andrew GelmanThis 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#### Spring 2025

##### Section/Call Number

001/14061##### Enrollment

0 of 35##### Instructor

Demissie Alemayehu##### Course Number

STAT5399G001##### Format

In-Person##### Points

1 pts#### Spring 2025

##### Section/Call Number

001/14062##### Enrollment

0 of 25##### Instructor

Demissie Alemayehu##### Course Number

STAT5703W001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

Tu 17:40-18:55Th 17:40-18:55

##### Section/Call Number

001/14063##### Enrollment

0 of 180##### Instructor

Dobrin Marchev##### Course Number

STAT6102G001##### Format

In-Person##### Points

4 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14064##### Enrollment

0 of 25##### Instructor

Yuqi Gu##### Course Number

STAT6104G001##### Format

In-Person##### Points

4 pts#### Spring 2025

##### Times/Location

Tu 14:10-16:00##### Section/Call Number

001/14065##### Enrollment

0 of 25##### Instructor

Liam PaninskiPrerequisites: 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#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14066##### Enrollment

0 of 15##### Instructor

Tian ZhengAshley Datta

##### Course Number

STAT6202G001##### Format

In-Person##### Points

4 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14067##### Enrollment

0 of 25##### Instructor

Cynthia Rush##### Course Number

STAT6302G001##### Format

In-Person##### Points

4 pts#### Spring 2025

##### Times/Location

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

##### Section/Call Number

001/14068##### Enrollment

0 of 25##### Instructor

Marcel Nutz.

##### Course Number

STAT8101G001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14069##### Enrollment

0 of 25##### Instructor

Christopher Harshaw##### Course Number

STAT9201G001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14070##### Enrollment

0 of 45##### Instructor

Yuqi GuBianca Dumitrascu

##### Course Number

STAT9301G001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14071##### Enrollment

0 of 25##### Instructor

Ivan Corwin##### Course Number

STAT9302G001##### Format

In-Person##### Points

1 pts#### Spring 2025

##### Times/Location

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

001/14072##### Enrollment

0 of 25##### Instructor

Chenyang ZhongSumit Mukherjee

##### Course Number

STAT9303G001##### Format

In-Person##### Points

3 pts#### Spring 2025

##### Times/Location

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

001/14073##### Enrollment

0 of 25##### Instructor

Marcel NutzPhilip Protter