Course | Number | Instructor | Time | Room |
Introduction to Statistics |
S&DS 101-109/501-509 |
Jonathan Reuning-Scherer and Staff |
Tues, Thurs 1:00-2:15 |
YSB MARSH |
Data Exploration and Analysis |
S&DS 230/530 PLSC 530 |
Ethan Meyers |
Tues, Thurs 9:00-10:15 |
ML 211 |
(Bayesian) Probability and Statistics |
S&DS 238/538 |
Joe Chang |
Tues, Thurs 1:00-2:15 |
17HLH 101 - TEAL |
Probability for Data Science |
S&DS 240/540 |
Elisa Celis |
Mon, Wed 2:30-3:45 |
ML 211 |
Probability Theory with Applications |
S&DS 241/541 MATH 241 |
Yihong Wu |
Mon, Wed 9:00-10:15 |
DAVIES AUD |
Computational Tools for Data Science |
S&DS 262/562 |
Roy Lederman |
Mon, Wed 1:00-2:15 |
DL 220 |
Introductory Machine Learning |
S&DS 265/565 |
John Lafferty |
Tues, Thurs 9:00-10:15 |
WLH 201 |
Linear Models |
S&DS 312/612 |
David Brinda |
Mon, Wed 11:35-12:50 |
DL 220 |
Advanced Probability |
S&DS 400/600 MATH 330 |
Sekhar Tatikonda |
Tues, Thurs 2:30-3:45 |
WTS A51 |
Statistical Inference |
S&DS 410/610 |
Zhou Fan |
Tues, Thurs 11:35-12:50 |
LUCE 202 |
Statistical Case Studies |
S&DS 425 |
Brian MacDonald |
Mon, Wed 2:30-3:45 |
17HLH 111 |
Senior Project |
S&DS 491 |
Sekhar Tatikonda |
- |
- |
Applied Machine Learning and Causal Inference Research Seminar |
S&DS 617 |
Jas Sekhon |
Wed 4:00-5:50 |
RKZ 06 |
Statistical Case Studies |
S&DS 625 |
Jay Emerson |
Mon, Wed 2:30-3:45 |
17HLH 101 - TEAL |
Computation and Optimization |
S&DS 431/631 |
Anna Gilbert |
Tues, Thurs 1:00-2:15 |
WTS A60 |
Statistical Computing |
S&DS 662 |
Jay Emerson |
Mon, Wed 9:00-10:15 |
17HLH 101 - TEAL |
Function Estimation |
S&DS 679 |
Andrew Barron |
Tues, Thurs 9:00 - 10:15 |
24 Hillhouse |
Indep Study |
S&DS 480ab |
Staff |
- |
- |
Practical Work |
S&DS 626ab |
DGS |
- |
- |
Statistical Consulting |
S&DS 627a/628b |
Jay Emerson |
Fri 2:30-4:30 |
24 Hillhouse |
Independent Study or Topics Course |
S&DS 690ab |
DGS |
- |
- |
Departmental Seminar |
S&DS 700ab |
- |
Mon 4:00-5:30 |
24 Hillhouse |
Introductory Statistics |
S&DS 100b/500b |
Ethan Meyers |
Tues, Thurs 9:00-10:15 |
TBD |
YData: An Introduction to Data Science |
S&DS 123b |
Ethan Meyers |
Mon, Wed, Fri 10:30-11:20 |
TBD |
Foreign Assistance to Sub-Saharan Africa: Archival Data Analysis |
S&DS 138b/AFST 378/EVST 378/AFST 570 |
Russell Barbour |
Tues, Thurs 2:30-3:45 |
TBD |
YData: Measuring Culture |
S&DS 175b/575b |
Daniel Karell |
Thurs 3:30-5:20 |
TBD |
YData: Humanities Data Mining |
S&DS 176b/576b |
Peter Leonard |
Tues, Thurs 1:00-2:15 |
TBD |
Intensive Introductory Statistics and Data Science |
S&DS 220b/520b |
Brian MacDonald |
Tues, Thurs 9:00-10:15 |
TBD |
Data Exploration and Analysis |
S&DS 230b/530b PLSC 530b |
Jonathan Reuning-Scherer |
Tues, Thurs 9:00-10:15 |
TBD |
Theory of Statistics |
S&DS 242b/542b |
Zhou Fan |
Mon, Wed 9:00-10:15 |
TBD |
Applied Machine Learning and Causal Inference |
S&DS 317b/517b |
Jas Sekhon |
Tues, Thurs 4:00-5:15 |
TBD |
Stochastic Processes |
S&DS 351b/551b |
Andrew Barron |
Mon, Wed 1:00-2:15 |
TBD |
Biomedical Data Science, Mining and Modeling |
S&DS 352/MCDB 452 |
Mark Gerstein and Matthew Simon |
Mon, Wed 1:00-2:15 |
|
Data Analysis |
S&DS 361b/661b |
Elena Khusainova |
Tues, Thurs 9:00-10:15 |
TBD |
Multivariate Statistics for Social Sciences |
S&DS 363b/563b |
Jonathan Reuning-Scherer |
Tues, Thurs 1:00-2:15 |
TBD |
Information Theory |
S&DS 364b/664b |
Yihong Wu |
Tues, Thurs 11:35-12:50 |
TBD |
Intermediate Machine Learning |
S&DS 365a/665a |
John Lafferty |
Mon, Wed 11:35-12:50 |
LC 101 |
Senior Capstone: Statistical Case Studies |
S&DS 425b |
Brian MacDonald |
Tues, Thurs 2:30-3:45 |
TBD |
Topics in Deep Learning: Methods and Biomedical Applications |
S&DS 567 CB&B 567 |
Martin Renqiang and Mark Gerstein |
Mon 9:00-11:15 |
TBD |
Selected Topics in Statistical Decision Theory |
S&DS 411a/611b |
Harrison Zhou |
Tues 3:30-5:20 |
TBD |
Advanced Optimization Techniques |
S&DS 432b/632b |
Sekhar Tatikonda |
Tues, Thurs 1:00-2:15 |
TBD |
Sum-of-Squares Optimization |
CPSC 663 |
TBA |
Mon, Wed 1:00-2:15 |
WTS A68 |
Applied Spatial Statistics |
S&DS 674b/F&ES 781b |
Tim Gregoire |
Tues, Thurs 10:30-11:50 |
TBD |
Statistics and Data Science Computing Laboratory (1/2 credit) |
S&DS 110b/510b |
not taught this year |
Theory of Probability and Statistics |
S&DS 239a/539a |
not taught this year |
Design and Analysis of Algorithms |
CPSC 365b |
not taught this year |
Optimization Techniques |
S&DS 430a/630a ENAS 530a EENG 437a ECON 413a |
not taught this year |
Senior Seminar and Project |
S&DS 490a |
not taught this year |
Senior Project |
S&DS 492b |
not taught this year |
Research Design and Causal Inference |
PLSC 508a |
not taught this year |
Applied Linear Models |
S&DS 531a |
not taught this year |
Intensive Algorithms |
S&DS 566 |
not taught this year |
Introduction to Random Matrix Theory and Applications |
S&DS 615b |
not taught this year |
Spectral Graph Theory |
CPSC 662a |
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 |
Topics on Random Graphs |
MATH 670 |
not taught this year |
Information Theory Tools in Probability and Statistics |
S&DS 672a |
not taught this year |
Topological Data Analysis |
S&DS 675a |
not taught this year |
Signal Processing for Data Science |
S&DS 676b |
not taught this year |
High-Dimensional Function Estimation (prev title) |
S&DS 682a |
not taught this year |
Statistical Methods in Neuroimaging |
S&DS 683a |
not taught this year |
Research Seminar in Probability |
S&DS 699ab |
not taught this year |
Placeholder -- Monograph |
706 |
not taught this year |