Course | Number | Instructor | Time | Room |
Introduction to Statistics |
S&DS 101a-106a/501a-506a |
Jonathan Reuning-Scherer and Staff |
Tues, Thurs 1:00-2:15 |
OML 202 |
Data Exploration and Analysis |
S&DS 230a/530a PLSC 530a |
Susan Wang |
Tues, Thurs 9:00-10:15 |
DL 220 |
(Bayesian) Probability and Statistics |
S&DS 238a/538a |
Joe Chang |
Tues, Thurs 1:00-2:15 |
ML 211 |
Probability Theory with Applications |
S&DS 241a/541a MATH 241a |
Yihong Wu |
Mon, Wed 9:00-10:15 |
Davies Aud |
Linear Models |
S&DS 312a/612a |
David Brinda |
Mon, Wed 11:35-12:50 |
WTS A60 |
Introduction to Causal Inference |
S&DS 314a |
Winston Lin |
Tues, Thurs 4:00-5:15 |
WLH 211 |
Applied Data Mining and Machine Learning |
S&DS 365a/565a |
John Lafferty and Derek Feng |
Tues, Thurs 9:00-10:15 |
Loria 250 |
Statistical Inference |
S&DS 410a/610a |
Zhou Fan |
Tues, Thurs 11:35-12:50 |
WTS B52 |
Optimization Techniques |
S&DS 430a/630a ENAS 530a EENG 437a ECON 413a |
Sekhar Tatikonda |
Tues, Thurs 1:00-2:15 |
WLH 117 |
Senior Project |
S&DS 491a |
Sekhar Tatikonda |
- |
- |
Statistical Case Studies |
S&DS 625a |
Susan Wang |
Mon, Wed 1:00-2:15 |
WTS A74 |
Spectral Graph Theory |
CPSC 662a |
Dan Spielman |
Mon, Wed 2:30-3:45 |
WTS A60 |
Computational Mathematics for Data Science |
S&DS 663a |
Roy Lederman |
Mon, Wed 9:00-10:15 |
24 Hillhouse |
Topics on Random Graphs |
MATH 670 |
Mathias Schacht |
Thurs 8:00-10:00 |
17 HH Rm 03 |
Information Theory Tools in Probability and Statistics |
S&DS 672a |
Andrew Barron |
Tues 9:00-11:15 |
24 Hillhouse |
High-Dimensional Statistical Estimation |
S&DS 679a |
Sahand Negahban |
Tues 2:30-5:00 |
24 Hillhouse |
Statistical Methods in Neuroimaging |
S&DS 683a |
Dustin Scheinost and Joe Chang |
Wed, Fri 2:30-3:45 |
17 Hillhouse Rm 115 |
Statistical Inference on Graphs |
S&DS 684a |
Yihong Wu |
Wed 2:30-5:00 |
24 Hillhouse |
Indep Study |
S&DS 480ab |
Staff |
- |
- |
Practical Work |
S&DS 626ab |
DGS |
- |
- |
Statistical Consulting |
S&DS 627a/628b |
Derek Feng |
Fri 2:30-4:30 |
24 Hillhouse |
Independent Study or Topics Course |
S&DS 690ab |
DGS |
- |
- |
Research Seminar in Probability |
S&DS 699ab |
Sekhar Tatikonda and David Pollard |
Fri 11:00-1:00 |
24 Hillhouse |
Departmental Seminar |
S&DS 700ab |
- |
Mon 4:15-5:30 |
24 Hillhouse |
Introductory Statistics |
S&DS 100b/500b |
David Brinda |
Mon, Wed, Fri 10:30-11:20 |
TBA |
YaleData |
S&DS 123b |
Jessi Cisewski |
TBD |
TBD |
YData: Lab Course |
S&DS 124b |
Jessi Cisewski |
TBD |
TBD |
Intensive Introductory Statistics and Data Science |
S&DS 220b/520b |
Susan Wang |
Tues, Thurs 9:00-10:15 |
TBA |
Data Exploration and Analysis |
S&DS 230b/530b PLSC 530b |
Jonathan Reuning-Scherer |
Tues, Thurs 9:00-10:15 |
TBA |
Theory of Statistics |
S&DS 242b/542b |
Andrew Barron |
Mon, Wed, Fri 9:25-10:15 |
TBA |
Stochastic Processes |
S&DS 351b/551b |
Yihong Wu and Sahand Negahban |
Mon, Wed 1:00-2:15 |
TBA |
Data Analysis |
S&DS 361b/661b |
David Brinda |
Mon, Wed 2:30-3:45 |
TBA |
Multivariate Statistics for Social Sciences |
S&DS 363b/563b |
Jonathan Reuning-Scherer |
Tues, Thurs 1:00-2:15 |
KRN 301 |
Information Theory |
S&DS 364b/664b |
Andrew Barron |
Tues, Thurs 11:35-12:50 |
24 Hillhouse |
Applied Data Mining and Machine Learning |
S&DS 365b/665b |
Derek Feng |
Mon, Wed 11:35-12:50 |
SCL 160 |
Advanced Probability |
S&DS 400b/600b MATH 330b |
Sekhar Tatikonda |
Tues, Thurs 2:30-3:45 |
24 Hillhouse |
Senior Capstone: Statistical Case Studies |
S&DS 425b |
Susan Wang |
Tues, Thurs 11:35-12:50 |
TBA |
Senior Project |
S&DS 492b |
Sekhar Tatikonda |
- |
- |
Research Design and Causal Inference |
PLSC 508b |
Winston Lin |
TBD |
TBD |
Introduction (?) to Random Matrix Theory and Applications |
S&DS 615b |
Zhou Fan |
Tues Thur 1:00-2:15 |
24 Hillhouse |
Statistical Methods in Computational Biology |
S&DS 645b |
Hongyu Zhao |
Thur 1:00-2:50 |
TBD |
Selected Topics in Neural Nets |
S&DS 671b |
Harrison Zhou |
Wed 9:00-11:30 (tentative) |
24 Hillhouse |
Applied Spatial Statistics |
S&DS 674b/F&ES 781b |
Tim Gregoire |
Tues, Thurs 10:30-11:50 |
TBD |
An Introduction to R for Statistical Computing and Data Science (1/2 credit) |
S&DS 110a/510a |
not taught this year |
Statistics and Data Science Computing Laboratory (1/2 credit) |
S&DS 110b/510b |
not taught this year |
Computational Tools for Data Science |
S&DS 262b/562b |
not taught this year |
Design and Analysis of Algorithms |
CPSC 365b |
not taught this year |
Senior Seminar and Project |
S&DS 490b |
not taught this year |
Applied Linear Models |
S&DS 531a |
not taught this year |
Statistical Computing |
S&DS 662b |
not taught this year |
Probabilistic Networks, Algorithms, and Applications |
S&DS 667a |
not taught this year |
Nonparametric Estimation and Machine Learning |
S&DS 468b |
not taught this year |
Statistical Learning Theory |
S&DS 669b |
not taught this year |