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 2023
Times/Location
Tu 10:10-11:25Th 10:10-11:25Section/Call Number
001/13327Enrollment
59 of 75Instructor
Pratyay DattaTian ZhengCourse Number
STAT1001W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
002/13328Enrollment
69 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 2023
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
003/13329Enrollment
57 of 75Instructor
Musa ElbulokCourse Number
STAT1101W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 08:40-09:55Th 08:40-09:55Section/Call Number
001/13330Enrollment
82 of 86Instructor
Alexander ClarkCourse Number
STAT1101W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
002/13331Enrollment
69 of 86Instructor
Ha NguyenCourse Number
STAT1101W003Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
003/13332Enrollment
73 of 86Instructor
Dobrin MarchevCourse Number
STAT1201W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
001/13333Enrollment
81 of 86Instructor
Banu BaydilCourse Number
STAT1201W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 08:40-09:55Th 08:40-09:55Section/Call Number
002/13334Enrollment
76 of 86Instructor
David RiosPrerequisites: one semester of calculus. Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value, confidence intervals, maximum likelihood estimation. Serves as the pre-requisite for ECON W3412.
Course Number
STAT1201W003Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 14:40-15:55We 14:40-15:55Section/Call Number
003/13335Enrollment
90 of 82Instructor
Chenyang ZhongCourse Number
STAT1201W004Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
004/13336Enrollment
84 of 86Instructor
Banu BaydilCourse Number
STAT1202W001Format
In-PersonPoints
1 ptsFall 2023
Times/Location
Fr 10:10-12:00Section/Call Number
001/13337Enrollment
10 of 25Instructor
Ronald NeathCourse Number
STAT2102W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 16:10-17:25Th 16:10-17:25Section/Call Number
001/13338Enrollment
49 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 2023
Times/Location
Mo 14:40-15:55We 14:40-15:55Section/Call Number
001/13339Enrollment
28 of 86Instructor
Wayne LeeThis is a course in intermediate statistical inference techniques in the context of applied research
questions in data science. Assuming some prior exposure to probability and statistics, this course will
first introduce the student to the principles of Bayesian inference, then apply them in estimation and
prediction in the context of linear and generalized linear models, counting and classification, mixture and
multilevel models, including scientific computation (like MCMC methods). Students will also learn
about the main benefits of using Bayesian vs. frequentist methods, like naturally combining prior
information with the data; posterior probabilities as easier to interpret alternatives to p-values; parameter
estimation “pooling” in hierarchical model and so on.
Course Number
STAT3104W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
001/13340Enrollment
18 of 50Instructor
Dobrin MarchevCourse Number
STAT3105W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 14:40-15:55We 14:40-15:55Section/Call Number
001/13341Enrollment
40 of 86Instructor
Alex PijyanCourse Number
STAT3107W001Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
001/13342Enrollment
2 of 5Instructor
Anne van DelftCourse Number
STAT3107W002Format
In-PersonPoints
1 ptsFall 2023
Section/Call Number
002/20849Enrollment
2 of 2Instructor
Arian MalekiPrerequisites: the project mentors permission. This course provides a mechanism for students who undertake research with a faculty member from the Department of Statistics to receive academic credit. Students seeking research opportunities should be proactive and entrepreneurial: identify congenial faculty whose research is appealing, let them know of your interest and your background and skills.
Course Number
STAT3107W003Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
003/21119Enrollment
1 of 1Instructor
David RiosPrerequisites: Calculus through multiple integration and infinite sums. A calculus-based tour of the fundamentals of probability theory and statistical inference. Probability models, random variables, useful distributions, conditioning, expectations, law of large numbers, central limit theorem, point and confidence interval estimation, hypothesis tests, linear regression. This course replaces SIEO 4150.
Course Number
STAT4001W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-20:40Section/Call Number
001/13343Enrollment
140 of 189Instructor
Isabella SandersCourse Number
STAT4203W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 10:10-11:25We 10:10-11:25Section/Call Number
001/13344Enrollment
60 of 86Instructor
Richard DavisCourse Number
STAT4203W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
002/13345Enrollment
29 of 86Instructor
David RiosCourse Number
STAT4203W003Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
003/13346Enrollment
38 of 40Instructor
David RiosCourse Number
STAT4204W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
001/13347Enrollment
48 of 86Instructor
Cristian PasaricaCourse Number
STAT4204W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
002/13348Enrollment
1 of 15Instructor
Cristian PasaricaCourse Number
STAT4205W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 13:10-14:25We 13:10-14:25Section/Call Number
001/13349Enrollment
11 of 86Instructor
Steven CampbellCourse Number
STAT4205W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
002/13350Enrollment
16 of 25Instructor
Philip ProtterCourse Number
STAT4205W003Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 19:40-20:55We 19:40-20:55Section/Call Number
003/13351Enrollment
3 of 25Instructor
Jeonghoe LeeCourse Number
STAT4205W004Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 08:40-09:55Th 08:40-09:55Section/Call Number
004/13352Enrollment
21 of 25Instructor
Gabriel YoungCourse Number
STAT4205W005Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
005/13353Enrollment
12 of 25Instructor
Yuqi GuCourse Number
STAT4206W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Fr 10:10-12:40Section/Call Number
001/13354Enrollment
7 of 35Instructor
Wayne LeeCourse Number
STAT4207W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 11:40-12:55We 11:40-12:55Section/Call Number
001/13355Enrollment
24 of 35Instructor
Mark BrownCourse Number
STAT4221W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/13356Enrollment
12 of 35Instructor
Rongning WuCourse Number
STAT4224W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
001/13357Enrollment
23 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 2023
Times/Location
We 18:10-20:55Section/Call Number
001/13358Enrollment
15 of 25Instructor
Ying LiuTian ZhengCourse Number
STAT4261W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Fr 10:10-12:40Section/Call Number
001/13359Enrollment
7 of 25Instructor
Hammou El BarmiCourse Number
STAT4263G001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/13360Enrollment
5 of 35Instructor
Yisha YaoCourse Number
STAT4263G002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Sa 10:10-12:40Section/Call Number
002/13361Enrollment
5 of 35Instructor
Franz RembartCourse Number
STAT4264G001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 16:10-17:25We 16:10-17:25Section/Call Number
001/13362Enrollment
10 of 35Instructor
Graeme BakerCourse Number
STAT4264G002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
002/13363Enrollment
13 of 35Instructor
Lars NielsenCourse Number
STAT4291W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Fr 17:10-19:40Section/Call Number
001/13364Enrollment
6 of 25Instructor
Demissie AlemayehuPrerequisites: STAT GR5203 or the equivalent, and two semesters of calculus. Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals, maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.
Course Number
STAT5204W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
001/13370Enrollment
7 of 35Instructor
Cristian PasaricaCourse Number
STAT5205W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
001/13381Enrollment
94 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 2023
Times/Location
Mo 19:40-20:55We 19:40-20:55Section/Call Number
002/13382Enrollment
81 of 86Instructor
Jeonghoe LeePrerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.
Course Number
STAT5205W003Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 08:40-09:55Th 08:40-09:55Section/Call Number
003/13383Enrollment
75 of 86Instructor
Gabriel YoungPrerequisites: STAT GR5203 and GR5204 or the equivalent. Theory and practice of regression analysis, Simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Extensive use of the computer to analyse data.
Course Number
STAT5205W004Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
004/13384Enrollment
30 of 86Instructor
Yuqi GuCourse Number
STAT5206W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Fr 10:10-12:40Section/Call Number
001/13385Enrollment
80 of 125Instructor
Wayne LeeCourse Number
STAT5206W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Fr 10:10-12:40Section/Call Number
002/13386Enrollment
123 of 125Instructor
Yongchan KwonCourse Number
STAT5206W003Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Th 18:10-20:40Section/Call Number
003/13387Enrollment
66 of 125Instructor
Haiyuan WangCourse Number
STAT5207W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 11:40-12:55We 11:40-12:55Section/Call Number
001/13388Enrollment
24 of 86Instructor
Mark BrownCourse Number
STAT5221W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/13389Enrollment
57 of 86Instructor
Rongning WuCourse Number
STAT5224W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
001/13390Enrollment
17 of 86Instructor
Ronald NeathCourse Number
STAT5242W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 10:10-12:40Section/Call Number
001/13391Enrollment
84 of 86Instructor
Samory KpotufeKamiar Rahnama RadCourse Number
STAT5242W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
We 10:10-12:40Section/Call Number
002/13392Enrollment
70 of 86Instructor
Kamiar Rahnama RadSamory KpotufeCourse Number
STAT5242W003Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Th 16:10-18:40Section/Call Number
003/15627Enrollment
20 of 86Instructor
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 2023
Times/Location
We 18:10-20:55Section/Call Number
001/13393Enrollment
33 of 50Instructor
Ying LiuTian ZhengThis course is an optional companion lab course for GR5242 Advanced Machine Learning. The aim of this course is to help students acquire the basic computational skills in Tensorflow and Python to implement machine learning models. Lab class materials will be aligned closely with the topics covered in GR5242. Google Colab and Jupyter notebooks will be used as the main tools for the hands-on lab exercises.
Open to GR5242 students only.
Prerequisites
Some familiarity with python is assumed, but we will begin the class with a tutorial on 'Python for machine learning'.
Course Number
STAT5245G001Format
In-PersonPoints
1 ptsFall 2023
Times/Location
Fr 17:35-18:35Section/Call Number
001/13990Enrollment
42 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 Tensorflow and Python to implement machine learning models. Lab class materials will be aligned closely with the topics covered in GR5242. Google Colab and Jupyter notebooks will be used as the main tools for the hands-on lab exercises.
Open to GR5242 students only.
Prerequisites
Some familiarity with python is assumed, but we will begin the class with a tutorial on 'Python for machine learning'.
Course Number
STAT5245G002Format
In-PersonPoints
1 ptsFall 2023
Times/Location
Fr 18:45-19:45Section/Call Number
002/13991Enrollment
0 of 86Instructor
Ka-Yi NgCourse Number
STAT5261W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Fr 10:10-12:40Section/Call Number
001/13396Enrollment
66 of 125Instructor
Hammou El BarmiPrerequisites: STAT GR5205 or the equivalent. Available to SSP, SMP Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering.
Course Number
STAT5263G001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/13397Enrollment
55 of 100Instructor
Yisha YaoPrerequisites: STAT GR5205 or the equivalent. Available to SSP, SMP Modeling and inference for random processes, from natural sciences to finance and economics. ARMA, ARCH, GARCH and nonlinear models, parameter estimation, prediction and filtering.
Course Number
STAT5263G002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Sa 10:10-12:40Section/Call Number
002/13398Enrollment
62 of 100Instructor
Franz RembartCourse Number
STAT5264G001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 16:10-17:25We 16:10-17:25Section/Call Number
001/13399Enrollment
39 of 65Instructor
Graeme BakerCourse Number
STAT5264G002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-19:25We 18:10-19:25Section/Call Number
002/13400Enrollment
80 of 100Instructor
Lars NielsenCourse Number
STAT5291W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Fr 17:10-19:40Section/Call Number
001/13401Enrollment
55 of 325Instructor
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 2023
Times/Location
Mo 19:00-21:30Section/Call Number
001/13403Enrollment
13 of 86Instructor
Ori ShentalTopics in Modern Statistics will provide MA Statistics students with an opportunity to study a specialized area of statistics in more depth and to meet the educational needs of a rapidly growing field.
Course Number
STAT5293G002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 13:10-15:40Section/Call Number
002/13402Enrollment
50 of 66Instructor
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
STAT5293GOO4Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 18:10-20:40Section/Call Number
OO4/15225Enrollment
54 of 50Instructor
Lei KangThe course aims to teach MA in Statistics students how to manage their careers and develop professionally. Topics include resume and cover-letter writing, negotiation, mentoring, interviewing skills and communication across global teams. Top professionals from across the globe speak to students and help improve leadership skills.
Course Number
STAT5391G003Format
In-PersonPoints
0 ptsFall 2023
Times/Location
We 16:10-17:25Section/Call Number
003/13608Enrollment
36 of 40Instructor
Banu BaydilThe course aims to teach MA in Statistics students how to manage their careers and develop professionally. Topics include resume and cover-letter writing, negotiation, mentoring, interviewing skills and communication across global teams. Top professionals from across the globe speak to students and help improve leadership skills.
Course Number
STAT5391G005Format
In-PersonPoints
0 ptsFall 2023
Times/Location
Mo 11:40-12:55We 11:40-12:55Section/Call Number
005/Enrollment
0 of 25Instructor
Banu BaydilThis course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.
Course Number
STAT5398G001Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
001/13404Enrollment
27 of 25Instructor
Demissie AlemayehuThis course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.
Course Number
STAT5398G002Format
In-PersonPoints
3 ptsThis course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.
Course Number
STAT5398G003Format
On-Line OnlyPoints
3 ptsThis course is intended to provide a mechanism to MA students in Statistics who undertake on-campus project work or research. The course may be signed up with a faculty member from the Department of Statistics for academic credit. Students seeking to enroll in the course should identify an on-campus project and a congenial faculty member whose research is appealing to them, and who are able to serve as their mentor. Students should then submit an application to enroll in this course, which will be reviewed and approved by the Faculty Director of the MA in Statistics program.
Course Number
STAT5398G004Format
In-PersonPoints
3 ptsCourse Number
STAT5399G001Format
In-PersonPoints
1 ptsFall 2023
Section/Call Number
001/13405Enrollment
16 of 25Instructor
Demissie AlemayehuPrerequisites: Calculus This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.
Course Number
STAT5701W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Fr 17:30-20:00Section/Call Number
001/13406Enrollment
105 of 120Instructor
Tat Sang FungPrerequisites: Calculus This course covers the following topics: Fundamentals of probability theory and statistical inference used in data science; Probabilistic models, random variables, useful distributions, expectations, law of large numbers, central limit theorem; Statistical inference; point and confidence interval estimation, hypothesis tests, linear regression.
Course Number
STAT5701W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 19:40-20:55Th 19:40-20:55Section/Call Number
002/13407Enrollment
84 of 120Instructor
Alberto Gonzalez SanzPrerequisites: programming. This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.
Course Number
STAT5702W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 16:10-17:25We 16:10-17:25Section/Call Number
001/13408Enrollment
83 of 86Instructor
Joyce RobbinsPrerequisites: programming. This course is covers the following topics: fundamentals of data visualization, layered grammer of graphics, perception of discrete and continuous variables, intreoduction to Mondran, mosaic pots, parallel coordinate plots, introduction to ggobi, linked pots, brushing, dynamic graphics, model visualization, clustering and classification.
Course Number
STAT5702W002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 16:10-17:25Th 16:10-17:25Section/Call Number
002/13409Enrollment
79 of 86Instructor
Joyce RobbinsCourse Number
STAT5703W001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 18:10-19:25Th 18:10-19:25Section/Call Number
001/13410Enrollment
32 of 50Instructor
Dobrin MarchevCourse Number
STAT6101G001Format
In-PersonPoints
4 ptsFall 2023
Times/Location
Mo 10:10-11:25We 10:10-11:25Section/Call Number
001/13411Enrollment
15 of 25Instructor
Ming YuanCourse Number
STAT6103G001Format
In-PersonPoints
4 ptsFall 2023
Times/Location
Tu 10:10-11:25Th 10:10-11:25Section/Call Number
001/13412Enrollment
11 of 25Instructor
Bianca DumitrascuPrerequisites: STAT GR6102 or instructor permission. The Deparatments doctoral student consulting practicum. Students undertake pro bono consulting activities for Columbia community researchers under the tutelage of a faculty mentor.
Course Number
STAT6105G001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Mo 12:00-13:15Section/Call Number
001/13413Enrollment
4 of 15Instructor
Regina DolgoarshinnykhCourse Number
STAT6106G001Format
In-PersonPoints
4 ptsFall 2023
Times/Location
Mo 08:40-09:55We 08:40-09:55Section/Call Number
001/13414Enrollment
8 of 50Instructor
Andrew GelmanCourse Number
STAT6201G001Format
In-PersonPoints
4 ptsFall 2023
Times/Location
Tu 14:40-15:55Th 14:40-15:55Section/Call Number
001/13415Enrollment
16 of 25Instructor
Cynthia RushCourse Number
STAT6203G001Format
In-PersonPoints
4 ptsFall 2023
Times/Location
Th 18:10-20:40Section/Call Number
001/13416Enrollment
6 of 25Instructor
Anne van DelftCourse Number
STAT6301G001Format
In-PersonPoints
4 ptsFall 2023
Times/Location
Mo 14:40-15:55We 14:40-15:55Section/Call Number
001/13417Enrollment
17 of 25Instructor
Sumit MukherjeeCourse Number
STAT6303G001Format
In-PersonPoints
4 ptsFall 2023
Times/Location
Tu 10:10-11:25Th 10:10-11:25Section/Call Number
001/13418Enrollment
12 of 25Instructor
Marcel NutzIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R001Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
001/20907Enrollment
0 of 5Instructor
Marco Avella MedinaIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R002Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
002/20908Enrollment
2 of 5Instructor
David BleiIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R003Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
003/20909Enrollment
0 of 5Instructor
John CunninghamIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R004Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
004/20910Enrollment
2 of 5Instructor
Richard DavisIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R005Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
005/20911Enrollment
0 of 5Instructor
Victor de la PenaIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R006Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
006/20912Enrollment
1 of 5Instructor
Bianca DumitrascuIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R007Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
007/20913Enrollment
3 of 5Instructor
Andrew GelmanIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R008Format
In-PersonPoints
3 ptsIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R009Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
009/20931Enrollment
2 of 5Instructor
Ioannis KaratzasIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R010Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
010/20932Enrollment
2 of 5Instructor
Samory KpotufeIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R011Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
011/20933Enrollment
1 of 5Instructor
Jingchen LiuIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R012Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
012/20934Enrollment
0 of 5Instructor
Shaw-Hwa LoIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R013Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
013/20936Enrollment
4 of 5Instructor
Arian MalekiIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R014Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
014/20937Enrollment
3 of 5Instructor
Sumit MukherjeeIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R015Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
015/20938Enrollment
1 of 5Instructor
Marcel NutzIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R016Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
016/20939Enrollment
2 of 5Instructor
Liam PaninskiIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R017Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
017/20943Enrollment
1 of 5Instructor
Philip ProtterIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R018Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
018/20947Enrollment
0 of 5Instructor
Daniel RabinowitzIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R019Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
019/20950Enrollment
1 of 5Instructor
Cynthia RushIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R020Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
020/20952Enrollment
1 of 5Instructor
Bodhisattva SenIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R021Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
021/20958Enrollment
1 of 5Instructor
Michael SobelIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R022Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
022/20959Enrollment
0 of 5Instructor
Simon TavareIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R023Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
023/20960Enrollment
0 of 5Instructor
Anne van DelftIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R024Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
024/20961Enrollment
4 of 5Instructor
Zhiliang YingIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R025Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
025/20962Enrollment
2 of 5Instructor
Ming YuanIndependent Study with Faculty Advisor must be registered for every semester after first academic year
Course Number
STAT8001R026Format
In-PersonPoints
3 ptsFall 2023
Section/Call Number
026/20963Enrollment
3 of 5Instructor
Tian Zheng.
Course Number
STAT8101G001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Th 10:00-12:00Section/Call Number
001/13420Enrollment
9 of 25Instructor
John CunninghamCourse Number
STAT8201G001Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Fr 13:00-14:30Section/Call Number
001/13421Enrollment
7 of 30Instructor
Liam PaninskiCourse Number
STAT8201G002Format
In-PersonPoints
3 ptsFall 2023
Times/Location
Tu 14:30-16:30Section/Call Number
002/13422Enrollment
8 of 25Instructor
Marco Avella Medina.