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
STAT1001W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 14:40-15:55We 14:40-15:55
Section/Call Number
001/13986Enrollment
80 of 86Instructor
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
STAT1001W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 10:10-11:25Th 10:10-11:25
Section/Call Number
002/13987Enrollment
49 of 86Instructor
Ashley DattaA 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
STAT1001W003Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-19:25Th 18:10-19:25
Section/Call Number
003/13988Enrollment
76 of 86Instructor
Anthony DonoghueCourse Number
STAT1101W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Th 10:10-11:25Tu 10:10-11:25
Section/Call Number
001/13989Enrollment
88 of 160Instructor
Wayne LeeCourse Number
STAT1101W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 18:10-19:25We 18:10-19:25
Section/Call Number
002/13991Enrollment
103 of 120Instructor
Ha NguyenThis is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024.
Course Number
STAT1102W001Format
In-PersonPoints
0 ptsSpring 2025
Times/Location
Tu 12:10-13:00Section/Call Number
001/18954Enrollment
45 of 50This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024.
Course Number
STAT1102W002Format
In-PersonPoints
0 ptsSpring 2025
Times/Location
Tu 17:10-18:00Section/Call Number
002/18955Enrollment
46 of 50This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024.
Course Number
STAT1102W003Format
In-PersonPoints
0 ptsSpring 2025
Times/Location
We 16:10-17:00Section/Call Number
003/18956Enrollment
46 of 50This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024.
Course Number
STAT1102W004Format
In-PersonPoints
0 ptsSpring 2025
Times/Location
Th 18:10-19:00Section/Call Number
004/18957Enrollment
35 of 50This is only recitation for STAT UG1101. We are requesting 8 sections of recitation to align with the two sections of 1101 offered for Fall 2024.
Course Number
STAT1102W005Format
In-PersonPoints
0 ptsSpring 2025
Times/Location
Fr 09:10-10:00Section/Call Number
005/18958Enrollment
17 of 50Prerequisites: 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
STAT1201W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 10:10-11:25We 10:10-11:25
Section/Call Number
001/13992Enrollment
81 of 86Instructor
Hammou El BarmiCourse Number
STAT1201W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 10:10-11:25We 10:10-11:25
Section/Call Number
002/13993Enrollment
70 of 85Instructor
Joyce RobbinsCourse Number
STAT1201W003Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Th 10:10-11:25Tu 10:10-11:25
Section/Call Number
003/13994Enrollment
74 of 86Instructor
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
STAT1201W004Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 18:10-19:25We 18:10-19:25
Section/Call Number
004/13995Enrollment
138 of 180Instructor
Banu BaydilCourse Number
STAT2102W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 16:10-17:25Th 16:10-17:25
Section/Call Number
001/13996Enrollment
97 of 120Instructor
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
STAT2103W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 18:10-19:25We 18:10-19:25
Section/Call Number
001/13998Enrollment
53 of 85Instructor
Daniel RabinowitzCourse Number
STAT2104W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 08:40-09:55We 08:40-09:55
Section/Call Number
001/13999Enrollment
45 of 86Instructor
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
STAT3106W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 14:40-15:55Th 14:40-15:55
Section/Call Number
001/14000Enrollment
36 of 86Instructor
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
STAT3107W001Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
001/14001Enrollment
1 of 2Instructor
Anne van DelftPrerequisites: 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
STAT3107W002Format
In-PersonPoints
1 ptsSpring 2025
Section/Call Number
002/20528Enrollment
1 of 1Instructor
Joyce RobbinsPrerequisites: 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
STAT3107W003Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
003/20649Enrollment
1 of 5Instructor
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
STAT3107W004Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
004/20869Enrollment
1 of 1Instructor
Philip ProtterTopics 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
STAT3293W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 14:40-15:55Th 14:40-15:55
Section/Call Number
001/14074Enrollment
3 of 16Instructor
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
STAT4001W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-19:25Th 18:10-19:25
Section/Call Number
001/14003Enrollment
77 of 100Instructor
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
STAT4001W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 13:10-14:25We 13:10-14:25
Section/Call Number
002/14004Enrollment
39 of 86Instructor
Sumit MukherjeeCourse Number
STAT4203W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-19:25Th 18:10-19:25
Section/Call Number
001/14011Enrollment
82 of 116Instructor
Marco Avella MedinaCourse Number
STAT4203W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-19:25Th 18:10-19:25
Section/Call Number
002/14010Enrollment
2 of 3Instructor
Gabriel YoungCourse Number
STAT4204W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 13:10-14:25Th 13:10-14:25
Section/Call Number
001/14012Enrollment
39 of 45Instructor
Banu BaydilCourse Number
STAT4204W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 19:10-20:25Th 19:10-20:25
Section/Call Number
002/14013Enrollment
25 of 35Instructor
Ashley DattaCourse Number
STAT4204W003Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 19:10-20:25Th 19:10-20:25
Section/Call Number
003/17906Enrollment
22 of 25Instructor
Ashley DattaCourse Number
STAT4205W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 18:10-19:25We 18:10-19:25
Section/Call Number
001/14014Enrollment
31 of 50Instructor
Ronald NeathCourse Number
STAT4206W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Fr 10:10-12:40Section/Call Number
001/14015Enrollment
6 of 10Instructor
Yongchan KwonCourse Number
STAT4207W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 16:10-17:25Th 16:10-17:25
Section/Call Number
001/14016Enrollment
39 of 50Instructor
Anne van DelftCourse Number
STAT4207W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 11:40-12:55We 11:40-12:55
Section/Call Number
002/14017Enrollment
11 of 35Instructor
Adam JaffeCourse Number
STAT4221W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Sa 10:10-12:40Section/Call Number
001/14018Enrollment
14 of 45Instructor
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
STAT4222W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 17:30-18:45We 17:30-18:45
Section/Call Number
001/14019Enrollment
1 of 25Instructor
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
STAT4224W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 19:40-20:55Th 19:40-20:55
Section/Call Number
001/14020Enrollment
15 of 30Instructor
Dobrin MarchevCourse Number
STAT4231W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Fr 13:10-15:40Section/Call Number
001/17402Enrollment
1 of 15Instructor
Zhiliang YingCourse Number
STAT4234W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 14:40-15:55Th 14:40-15:55
Section/Call Number
001/14021Enrollment
6 of 25Instructor
Rongning WuCourse Number
STAT4241W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 10:10-11:25We 10:10-11:25
Section/Call Number
001/14024Enrollment
15 of 50Instructor
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
STAT4243W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
We 18:10-20:55Section/Call Number
001/14028Enrollment
21 of 25Instructor
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
STAT4243W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Th 18:10-20:55Section/Call Number
002/14029Enrollment
7 of 25Instructor
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
STAT4243W003Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 16:10-18:40Section/Call Number
003/14030Enrollment
3 of 5Instructor
Galen McKinleyTian Zheng
Prerequisites: 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
STAT4243W004Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Th 16:10-18:40Section/Call Number
004/17401Enrollment
5 of 25Instructor
Bianca DumitrascuCourse Number
STAT4261W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Sa 10:10-12:40Section/Call Number
001/14031Enrollment
18 of 25Instructor
Zhiliang YingCourse Number
STAT4264G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 16:10-17:25We 16:10-17:25
Section/Call Number
001/14032Enrollment
6 of 25Instructor
Steven CampbellCourse Number
STAT4265G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-19:25Th 18:10-19:25
Section/Call Number
001/14033Enrollment
6 of 25Instructor
Graeme BakerCourse Number
STAT4291W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Fr 10:10-12:40Section/Call Number
001/14034Enrollment
5 of 25Instructor
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
STAT5203W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-19:25Th 18:10-19:25
Section/Call Number
002/14035Enrollment
39 of 45Instructor
Gabriel YoungPrerequisites: 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
STAT5204W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 19:10-20:25Th 19:10-20:25
Section/Call Number
002/14037Enrollment
5 of 15Instructor
Ashley DattaPrerequisites: 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
STAT5205W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 18:10-19:25We 18:10-19:25
Section/Call Number
001/14038Enrollment
92 of 110Instructor
Yisha YaoPrerequisites: 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
STAT5205W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-19:25Th 18:10-19:25
Section/Call Number
002/20372Enrollment
0 of 86Course Number
STAT5206W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Fr 10:10-12:40Section/Call Number
001/14039Enrollment
73 of 80Instructor
Yongchan KwonCourse Number
STAT5207W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 11:40-12:55We 11:40-12:55
Section/Call Number
001/14040Enrollment
20 of 100Instructor
Adam JaffeCourse Number
STAT5221W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Sa 10:10-12:40Section/Call Number
001/14041Enrollment
18 of 100Instructor
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
STAT5222W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 17:30-18:45We 17:30-18:45
Section/Call Number
001/14042Enrollment
16 of 86Instructor
Arian MalekiCourse Number
STAT5224W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 19:40-20:55Th 19:40-20:55
Section/Call Number
001/14043Enrollment
16 of 125Instructor
Dobrin MarchevCourse Number
STAT5231W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Fr 13:10-15:40Section/Call Number
001/17403Enrollment
36 of 50Instructor
Zhiliang YingCourse Number
STAT5234W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 14:40-15:55Th 14:40-15:55
Section/Call Number
001/14044Enrollment
69 of 86Instructor
Rongning WuCourse Number
STAT5241W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 10:10-11:25We 10:10-11:25
Section/Call Number
001/14045Enrollment
117 of 160Instructor
Genevera AllenCourse Number
STAT5241W002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-19:25Th 18:10-19:25
Section/Call Number
002/14046Enrollment
0 of 86Instructor
Yisha YaoCourse Number
STAT5241W003Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 18:10-19:25We 18:10-19:25
Section/Call Number
003/14047Enrollment
5 of 86Instructor
Alberto Gonzalez SanzCourse Number
STAT5241W004Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 13:10-14:25Th 13:10-14:25
Section/Call Number
004/14048Enrollment
79 of 86Instructor
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
STAT5243W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
We 18:10-20:55Section/Call Number
001/14049Enrollment
42 of 86Instructor
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
STAT5243W002Points
3 ptsSpring 2025
Times/Location
Th 18:10-20:55Section/Call Number
002/14050Enrollment
68 of 86Instructor
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
STAT5243W003Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 16:10-18:40Section/Call Number
003/14051Enrollment
2 of 15Instructor
Galen McKinleyTian Zheng
Prerequisites: 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
STAT5243W004Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Th 16:10-18:40Section/Call Number
004/17404Enrollment
15 of 35Instructor
Bianca DumitrascuCourse Number
STAT5261W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Sa 10:10-12:40Section/Call Number
001/14052Enrollment
101 of 170Instructor
Zhiliang YingCourse Number
STAT5264G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 16:10-17:25We 16:10-17:25
Section/Call Number
001/14053Enrollment
31 of 86Instructor
Steven CampbellCourse Number
STAT5265G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-19:25Th 18:10-19:25
Section/Call Number
001/14054Enrollment
109 of 135Instructor
Graeme BakerCourse Number
STAT5291W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Fr 10:10-12:40Section/Call Number
001/14055Enrollment
160 of 225Instructor
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
STAT5293G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 14:40-15:55Th 14:40-15:55
Section/Call Number
001/14056Enrollment
17 of 86Instructor
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
STAT5293G002Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 13:10-14:25Th 13:10-14:25
Section/Call Number
002/14057Enrollment
14 of 35Instructor
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
STAT5293G003Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Fr 16:10-18:40Section/Call Number
003/14058Enrollment
38 of 86Instructor
Parijat DubeChen Wang
Topics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.
Course Number
STAT5293G004Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
We 18:10-20:40Section/Call Number
004/14059Enrollment
37 of 40Instructor
Lei KangTopics 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
STAT5293G005Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 10:10-11:25We 10:10-11:25
Section/Call Number
005/14060Enrollment
12 of 20Instructor
Andrew GelmanTopics 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
STAT5293G006Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 18:10-20:40Section/Call Number
006/17405Enrollment
17 of 60Instructor
Jeonghoe LeeThis 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
STAT5398G001Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
001/14061Enrollment
35 of 55Instructor
Demissie AlemayehuCourse Number
STAT5399G001Format
In-PersonPoints
1 ptsSpring 2025
Section/Call Number
001/14062Enrollment
3 of 25Instructor
Demissie AlemayehuThis course is an introduction to probability and statistics for data science. Topics
include probability theory, probability distributions, simulations, parameters estima-
tion, hypothesis testing, simple regression. Python examples will be used throughout
the course for illustrations.
Course Number
STAT5700G001Format
In-PersonPoints
0 ptsSpring 2025
Times/Location
Mo 13:10-14:00Section/Call Number
001/18993Enrollment
65 of 65Instructor
Dobrin MarchevRuchira Ray
This course is an introduction to probability and statistics for data science. Topics
include probability theory, probability distributions, simulations, parameters estima-
tion, hypothesis testing, simple regression. Python examples will be used throughout
the course for illustrations.
Course Number
STAT5700G002Format
In-PersonPoints
0 ptsSpring 2025
Times/Location
Tu 12:10-13:00Section/Call Number
002/18994Enrollment
53 of 65Instructor
Dobrin MarchevVictor Daniel
This course is an introduction to probability and statistics for data science. Topics
include probability theory, probability distributions, simulations, parameters estima-
tion, hypothesis testing, simple regression. Python examples will be used throughout
the course for illustrations.
Course Number
STAT5700G003Format
In-PersonPoints
0 ptsSpring 2025
Times/Location
We 17:10-18:00Section/Call Number
003/18995Enrollment
46 of 65Instructor
Dobrin MarchevGetoar Sopa
Course Number
STAT5703W001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 17:40-18:55Th 17:40-18:55
Section/Call Number
001/14063Enrollment
169 of 180Instructor
Dobrin MarchevCourse Number
STAT6102G001Format
In-PersonPoints
4 ptsSpring 2025
Times/Location
Mo 10:10-11:25We 10:10-11:25
Section/Call Number
001/14064Enrollment
22 of 25Instructor
Yuqi GuCourse Number
STAT6104G001Format
In-PersonPoints
4 ptsSpring 2025
Times/Location
Tu 14:10-16:00Section/Call Number
001/14065Enrollment
27 of 25Instructor
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
STAT6105G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 09:10-11:40Section/Call Number
001/14066Enrollment
7 of 15Instructor
Tian ZhengAshley Datta
Course Number
STAT6202G001Format
In-PersonPoints
4 ptsSpring 2025
Times/Location
Mo 14:40-15:55We 14:40-15:55
Section/Call Number
001/14067Enrollment
24 of 25Instructor
Cynthia RushCourse Number
STAT6302G001Format
In-PersonPoints
4 ptsSpring 2025
Times/Location
Tu 10:10-11:25Th 10:10-11:25
Section/Call Number
001/14068Enrollment
8 of 25Instructor
Marcel NutzIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R001Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
001/20480Enrollment
1 of 5Instructor
Marco Avella MedinaCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R002Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
002/20481Enrollment
2 of 5Instructor
David BleiCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R003Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
003/20482Enrollment
2 of 5Instructor
John CunninghamCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R004Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
004/20483Enrollment
2 of 5Instructor
Richard DavisCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R005Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
005/20484Enrollment
0 of 5Instructor
Victor de la PenaCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R006Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
006/20485Enrollment
1 of 5Instructor
Cindy MeekinsBianca Dumitrascu
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R007Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
007/20486Enrollment
1 of 5Instructor
Andrew GelmanCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R008Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
008/20487Enrollment
1 of 5Instructor
Cindy MeekinsYuqi Gu
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R009Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
009/20488Enrollment
1 of 5Instructor
Ioannis KaratzasCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R010Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
010/20489Enrollment
0 of 5Instructor
Samory KpotufeCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R011Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
011/20490Enrollment
1 of 5Instructor
Jingchen LiuCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R013Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
013/20494Enrollment
2 of 5Instructor
Arian MalekiCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R014Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
014/20495Enrollment
3 of 5Instructor
Sumit MukherjeeCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R015Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
015/20496Enrollment
2 of 5Instructor
Marcel NutzCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R016Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
016/20497Enrollment
5 of 5Instructor
Liam PaninskiCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R017Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
017/20498Enrollment
1 of 5Instructor
Philip ProtterCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R018Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
018/20499Enrollment
0 of 5Instructor
Daniel RabinowitzCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R019Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
019/20500Enrollment
1 of 5Instructor
Cynthia RushCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R020Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
020/20501Enrollment
2 of 5Instructor
Bodhisattva SenCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R021Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
021/20502Enrollment
1 of 5Instructor
Michael SobelCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R022Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
022/20503Enrollment
1 of 5Instructor
Simon TavareCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R023Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
023/20504Enrollment
1 of 5Instructor
Cindy MeekinsAnne van Delft
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R024Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
024/20505Enrollment
4 of 5Instructor
Zhiliang YingCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R025Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
025/20506Enrollment
1 of 5Instructor
Ming YuanCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R026Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
026/20507Enrollment
1 of 5Instructor
Tian ZhengCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R027Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
027/20493Enrollment
0 of 5Instructor
Simon TavareCindy Meekins
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R028Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
028/20492Enrollment
1 of 5Instructor
Cindy MeekinsGenevera Allen
Independent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R029Format
In-PersonPoints
3 ptsSpring 2025
Section/Call Number
029/20491Enrollment
1 of 5Instructor
Cindy MeekinsChristopher Harshaw
.
Course Number
STAT8101G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 10:10-12:00Section/Call Number
001/14069Enrollment
5 of 25Instructor
Christopher HarshawCourse Number
STAT8292G001Format
In-PersonPoints
3 ptsCourse Number
STAT8301Q001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Tu 10:10-12:00Section/Call Number
001/17400Enrollment
7 of 25Instructor
Richard DavisCourse Number
STAT9201G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Mo 16:10-17:25Section/Call Number
001/14070Enrollment
49 of 50Instructor
Anne van DelftChristopher Harshaw
Course Number
STAT9301G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Fr 11:40-12:55Section/Call Number
001/14071Enrollment
0 of 25Instructor
Ivan CorwinCourse Number
STAT9302G001Format
In-PersonPoints
1 ptsSpring 2025
Times/Location
Th 13:10-14:25Section/Call Number
001/14072Enrollment
8 of 25Instructor
Sumit MukherjeeChenyang Zhong
Course Number
STAT9303G001Format
In-PersonPoints
3 ptsSpring 2025
Times/Location
Th 16:10-17:25Section/Call Number
001/14073Enrollment
8 of 25Instructor
Marcel NutzPhilip Protter