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 ptsFall 2024
Times/Location
Tu 10:10-11:25Th 10:10-11:25Section/Call Number
001/15145Enrollment
29 of 75Instructor
Pratyay DattaCourse Number
STAT1001W002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
002/15159Enrollment
52 of 75Instructor
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
STAT1001W003Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
003/15146Enrollment
12 of 75Instructor
Musa ElbulokCourse Number
STAT1101W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/15160Enrollment
18 of 86Instructor
Dobrin MarchevCourse Number
STAT1101W002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
002/15161Enrollment
10 of 200Instructor
Alex PijyanPrerequisites: 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 ptsFall 2024
Times/Location
Tu 08:40-09:55Th 08:40-09:55Section/Call Number
001/15162Enrollment
160 of 160Instructor
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
STAT1201W002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 14:40-15:55We 14:40-15:55Section/Call Number
002/15163Enrollment
86 of 86Instructor
Chenyang ZhongPrerequisites: 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
STAT1201W003Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
003/15164Enrollment
75 of 75Instructor
Tat Sang FungCourse Number
STAT1202W001Format
In-PersonPoints
1 ptsFall 2024
Times/Location
Fr 10:10-12:00Section/Call Number
001/15165Enrollment
2 of 25Instructor
Ronald NeathCorequisites: An introductory course in statistic (STAT UN1101 is recommended). This course is an introduction to R programming. After learning basic programming component, such as defining variables and vectors, and learning different data structures in R, students will, via project-based assignments, study more advanced topics, such as conditionals, modular programming, and data visualization. Students will also learn the fundamental concepts in computational complexity, and will practice writing reports based on their data analyses.
Course Number
STAT2102W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 16:10-17:25Th 16:10-17:25Section/Call Number
001/15166Enrollment
60 of 86Instructor
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 ptsFall 2024
Times/Location
Mo 14:40-15:55We 14:40-15:55Section/Call Number
001/15167Enrollment
33 of 86Instructor
Ronald NeathThis is a course in intermediate statistical inference techniques in the context of applied research
questions in data science. Assuming some prior exposure to probability and statistics, this course will
first introduce the student to the principles of Bayesian inference, then apply them in estimation and
prediction in the context of linear and generalized linear models, counting and classification, mixture and
multilevel models, including scientific computation (like MCMC methods). Students will also learn
about the main benefits of using Bayesian vs. frequentist methods, like naturally combining prior
information with the data; posterior probabilities as easier to interpret alternatives to p-values; parameter
estimation “pooling” in hierarchical model and so on.
Course Number
STAT3104W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
001/15168Enrollment
8 of 50Instructor
Dobrin MarchevCourse Number
STAT3105W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 14:40-15:55We 14:40-15:55Section/Call Number
001/15169Enrollment
26 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 ptsFall 2024
Section/Call Number
001/15170Enrollment
0 of 5Instructor
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
STAT3293W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 10:00-12:00Section/Call Number
001/15545Enrollment
0 of 5Instructor
Liam PaninskiPrerequisites: 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 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
001/15171Enrollment
95 of 200Instructor
Arian MalekiCourse Number
STAT4203W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 10:10-11:25We 10:10-11:25Section/Call Number
001/15172Enrollment
45 of 86Instructor
Richard DavisCourse Number
STAT4203W002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
002/15173Enrollment
33 of 86Instructor
Pratyay DattaCourse Number
STAT4203W003Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
003/15174Enrollment
0 of 35Instructor
Pratyay DattaCourse Number
STAT4204W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
001/15175Enrollment
39 of 86Instructor
Michael SobelCourse Number
STAT4204W002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
002/15176Enrollment
0 of 5Instructor
Michael SobelCourse Number
STAT4205W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 13:10-14:25Th 13:10-14:25Section/Call Number
001/15147Enrollment
17 of 75Instructor
Jingchen LiuCourse Number
STAT4205W002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
002/15148Enrollment
19 of 25Instructor
Philip ProtterCourse Number
STAT4205W003Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 19:40-20:55We 19:40-20:55Section/Call Number
003/15177Enrollment
3 of 25Course Number
STAT4205W004Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 08:40-09:55Th 08:40-09:55Section/Call Number
004/15178Enrollment
8 of 25Instructor
Yisha YaoCourse Number
STAT4205W005Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
005/15179Enrollment
8 of 25Instructor
Yuqi GuCourse Number
STAT4206W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 10:10-12:40Section/Call Number
001/15180Enrollment
10 of 35Instructor
Wayne LeeCourse Number
STAT4207W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 11:40-12:55We 11:40-12:55Section/Call Number
001/15181Enrollment
17 of 35Instructor
Mark BrownCourse Number
STAT4221W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
001/15182Enrollment
11 of 35Instructor
Rongning WuThis 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 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
001/15183Enrollment
19 of 35Instructor
Ronald NeathPrerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will be covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.
Course Number
STAT4243W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
We 18:10-20:55Section/Call Number
001/15184Enrollment
15 of 15Instructor
Bianca DumitrascuCourse Number
STAT4261W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 10:10-12:40Section/Call Number
001/15185Enrollment
17 of 35Instructor
Hammou El BarmiCourse Number
STAT4263G001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/15186Enrollment
2 of 35Instructor
Alberto Gonzalez SanzCourse Number
STAT4263G002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Sa 10:10-12:40Section/Call Number
002/15187Enrollment
0 of 35Instructor
Franz RembartCourse Number
STAT4264G001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 16:10-17:25We 16:10-17:25Section/Call Number
001/15188Enrollment
7 of 35Instructor
Graeme BakerCourse Number
STAT4264G002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
002/15189Enrollment
3 of 35Course Number
STAT4265G001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 16:10-17:25We 16:10-17:25Section/Call Number
001/15190Enrollment
4 of 35Instructor
Steven CampbellCourse Number
STAT4291W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 17:10-19:40Section/Call Number
001/15149Enrollment
6 of 35Instructor
Demissie AlemayehuPrerequisites: 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
STAT5203W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/15152Enrollment
1 of 15Instructor
Pratyay DattaPrerequisites: 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
STAT5204W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
001/15193Enrollment
0 of 15Instructor
Michael SobelCourse Number
STAT5205W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
001/15157Enrollment
18 of 86Instructor
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
STAT5205W002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 19:40-20:55We 19:40-20:55Section/Call Number
002/15156Enrollment
0 of 86Prerequisites: 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
STAT5205W003Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 08:40-09:55Th 08:40-09:55Section/Call Number
003/15154Enrollment
0 of 86Instructor
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
STAT5205W004Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
004/15155Enrollment
6 of 86Instructor
Yuqi GuCourse Number
STAT5206W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 10:10-12:40Section/Call Number
001/15194Enrollment
0 of 125Instructor
Wayne LeeCourse Number
STAT5206W002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 10:10-12:40Section/Call Number
002/15195Enrollment
2 of 125Instructor
Yongchan KwonCourse Number
STAT5206W003Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 18:10-20:40Section/Call Number
003/15158Enrollment
2 of 125Instructor
Haiyuan WangCourse Number
STAT5207W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 11:40-12:55We 11:40-12:55Section/Call Number
001/15196Enrollment
10 of 86Instructor
Mark BrownCourse Number
STAT5221W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
001/15197Enrollment
67 of 86Instructor
Rongning WuThis course introduces the Bayesian paradigm for statistical inference. Topics covered include prior and posterior distributions: conjugate priors, informative and non-informative priors; one- and two-sample problems; models for normal data, models for binary data, Bayesian linear models, Bayesian computation: MCMC algorithms, the Gibbs sampler; hierarchical models; hypothesis testing, Bayes factors, model selection; use of statistical software.
Prerequisites: A course in the theory of statistical inference, such as STAT GU4204/GR5204 a course in statistical modeling and data analysis such as STAT GU4205/GR5205.
Course Number
STAT5224W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
001/15198Enrollment
27 of 86Instructor
Ronald NeathCourse Number
STAT5242W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 10:10-12:40Section/Call Number
001/15199Enrollment
125 of 125Instructor
Samory KpotufeKamiar Rahnama RadCourse Number
STAT5242W003Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Th 16:10-18:40Section/Call Number
003/15200Enrollment
65 of 125Instructor
Parijat DubePrerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications.
Course Number
STAT5243W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
We 18:10-20:55Section/Call Number
001/15201Enrollment
15 of 15Instructor
Bianca DumitrascuThis 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 a python-based Deep Learning library (such as Troch, TensorFlow) to implement deep learning models. lab class materials will be aligned closely with the topics covered in GR5242. Google Colab will be used as the main tools for the hands-on lab exercises.
Course Number
STAT5245G001Format
In-PersonPoints
1 ptsFall 2024
Times/Location
Th 17:35-18:35Section/Call Number
001/15202Enrollment
23 of 86Instructor
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 a python-based Deep Learning library (such as Troch, TensorFlow) to implement deep learning models. lab class materials will be aligned closely with the topics covered in GR5242. Google Colab will be used as the main tools for the hands-on lab exercises.
Course Number
STAT5245G002Format
In-PersonPoints
1 ptsFall 2024
Times/Location
Th 18:45-19:45Section/Call Number
002/15203Enrollment
28 of 86Instructor
Ka-Yi NgCourse Number
STAT5261W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 10:10-12:40Section/Call Number
001/15405Enrollment
19 of 125Instructor
Hammou El BarmiAvailable 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
STAT5263G001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/15204Enrollment
12 of 100Instructor
Alberto Gonzalez SanzAvailable 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
STAT5263G002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Sa 10:10-12:40Section/Call Number
002/15205Enrollment
13 of 100Instructor
Franz RembartCourse Number
STAT5264G001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 16:10-17:25We 16:10-17:25Section/Call Number
001/15206Enrollment
8 of 100Instructor
Graeme BakerCourse Number
STAT5264G002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
002/15207Enrollment
3 of 100Instructor
Lars NielsenCourse Number
STAT5265G001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 16:10-17:25We 16:10-17:25Section/Call Number
001/15208Enrollment
11 of 75Instructor
Steven CampbellCourse Number
STAT5291W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 17:10-19:40Section/Call Number
001/15209Enrollment
79 of 200Instructor
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
STAT5293G001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 13:10-15:40Section/Call Number
001/15210Enrollment
60 of 60Instructor
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
STAT5293G002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 18:10-20:40Section/Call Number
002/15211Enrollment
60 of 60Instructor
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
STAT5293G003Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Fr 10:00-12:00Section/Call Number
003/15546Enrollment
0 of 25Instructor
Liam PaninskiThis upcoming fall, we are going to kick off the “Practitioners Seminar” course, where successful practitioners from various industry fields (tech, finance, insurance, pharmaceutical, etc..) will have a chance to meet our students and present the projects they work on, technologies they utilize to achieve their goals, solutions they came up with etc. In addition, guest speakers will share their career development path (what kind of obstacles they faced, what pitfalls to avoid, and in general give advice on career development in their fields). We will finish up the meeting with a Q&A session with students.
Course Number
STAT5390G001Format
In-PersonPoints
0 ptsFall 2024
Times/Location
Fr 15:10-16:55Section/Call Number
001/15212Enrollment
1 of 50Instructor
Gabriel YoungThis 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 ptsFall 2024
Section/Call Number
001/15214Enrollment
3 of 25Instructor
Demissie AlemayehuCourse Number
STAT5399G001Format
In-PersonPoints
1 ptsFall 2024
Section/Call Number
001/15215Enrollment
1 of 25Instructor
Demissie AlemayehuThis 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
STAT5701W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 13:10-14:40We 13:10-14:40Section/Call Number
001/15216Enrollment
0 of 175Instructor
Dobrin MarchevThis 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
STAT5702W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Mo 16:10-17:25We 16:10-17:25Section/Call Number
001/15217Enrollment
1 of 60Instructor
Joyce RobbinsThis 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
STAT5702W002Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 16:10-17:25Th 16:10-17:25Section/Call Number
002/15218Enrollment
3 of 60Instructor
Joyce RobbinsCourse Number
STAT5703W001Format
In-PersonPoints
3 ptsFall 2024
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/15219Enrollment
19 of 50Instructor
Marco Avella MedinaCourse Number
STAT6101G001Format
In-PersonPoints
4 ptsFall 2024
Times/Location
Tu 10:10-11:25Th 10:10-11:25Section/Call Number
001/15220Enrollment
1 of 25Instructor
Andrew GelmanCourse Number
STAT6103G001Format
In-PersonPoints
4 ptsFall 2024
Times/Location
Tu 10:10-11:25Th 10:10-11:25Section/Call Number
001/15221Enrollment
8 of 25Instructor
John CunninghamPrerequisites: 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 ptsFall 2024
Times/Location
Th 12:00-13:10Section/Call Number
001/15222Enrollment
2 of 15Instructor
Tian ZhengCourse Number
STAT6201G001Format
In-PersonPoints
4 ptsFall 2024
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
001/15223Enrollment
2 of 25Instructor
Cynthia RushCourse Number
STAT6203G001Format
In-PersonPoints
4 ptsFall 2024
Times/Location
Mo 10:10-12:00Section/Call Number
001/15224Enrollment
8 of 25Instructor
Ming YuanCourse Number
STAT6301G001Format
In-PersonPoints
4 ptsFall 2024
Times/Location
Mo 14:40-15:55We 14:40-15:55Section/Call Number
001/15225Enrollment
1 of 25Instructor
Anne van DelftIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R001Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
001/15226Enrollment
0 of 5Instructor
Marco Avella MedinaCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R002Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
002/15227Enrollment
1 of 5Instructor
David BleiCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R003Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
003/15228Enrollment
0 of 5Instructor
John CunninghamCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R004Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
004/15229Enrollment
1 of 5Instructor
Richard DavisCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R005Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
005/15230Enrollment
0 of 5Instructor
Victor de la PenaCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R006Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
006/15231Enrollment
1 of 5Instructor
Bianca DumitrascuCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R007Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
007/15232Enrollment
0 of 5Instructor
Andrew GelmanCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R008Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
008/15233Enrollment
0 of 5Instructor
Yuqi GuCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R009Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
009/15234Enrollment
2 of 5Instructor
Ioannis KaratzasCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R010Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
010/15235Enrollment
0 of 5Instructor
Samory KpotufeCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R011Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
011/15236Enrollment
0 of 5Instructor
Jingchen LiuCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R012Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
012/15239Enrollment
0 of 5Instructor
Shaw-Hwa LoCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R013Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
013/15237Enrollment
1 of 5Instructor
Arian MalekiCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R014Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
014/15238Enrollment
1 of 5Instructor
Sumit MukherjeeCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R015Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
015/15240Enrollment
0 of 5Instructor
Marcel NutzCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R016Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
016/15241Enrollment
0 of 5Instructor
Liam PaninskiCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R017Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
017/15242Enrollment
1 of 5Instructor
Philip ProtterCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R018Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
018/15243Enrollment
0 of 5Instructor
Daniel RabinowitzCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R019Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
019/15244Enrollment
0 of 5Instructor
Cynthia RushCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R020Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
020/15245Enrollment
0 of 5Instructor
Bodhisattva SenCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R021Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
021/15246Enrollment
1 of 5Instructor
Michael SobelCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R022Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
022/15247Enrollment
0 of 5Instructor
Simon TavareCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R023Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
023/15248Enrollment
1 of 5Instructor
Anne van DelftCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R024Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
024/15249Enrollment
2 of 5Instructor
Zhiliang YingCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R025Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
025/15250Enrollment
1 of 5Instructor
Ming YuanCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R026Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
026/15251Enrollment
1 of 5Instructor
Tian ZhengCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R027Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
027/15542Enrollment
0 of 5Instructor
Simon TavareCindy MeekinsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R028Format
In-PersonPoints
3 ptsFall 2024
Section/Call Number
028/15252Enrollment
0 of 5Instructor
Genevera AllenCindy Meekins.