Department of Statistics and Data Science
Course List for Fall 2019/Spring 2020

Revised: 20 December 2019
Courses whose numbers end with a are offered in the FALL. Courses whose numbers end with b are offered in the SPRING.
Courses whose numbers end with ab are offered both semesters. Courses with a gray background are not taught this year.

CourseNumberInstructorTimeRoom
Introduction to Statistics S&DS 101a-109a/501a-509a Jonathan Reuning-Scherer and Staff Tues, Thurs 1:00-2:15 OML 202
Data Exploration and Analysis S&DS 230a/530a PLSC 530a Ethan Meyers 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 Winston Lin Mon, Wed 9:00-10:15 SSS 114
Linear Models S&DS 312a/612a David Brinda Mon, Wed 11:35-12:50 WLH 116
Measuring Impact and Opinion Change S&DS 315a/PLSC 340a Josh Kalla Tues, Thurs 2:30-3:45 TBD
Introduction to Data Mining and Machine Learning S&DS 355a/555a John Lafferty Tues, Thurs 9:00-10:15 OML 202
Applied Data Mining and Machine Learning S&DS 365a/565a Derek Feng Tues, Thurs 9:00-10:15 LC 101
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 WTS A51
Senior Project S&DS 491a Sekhar Tatikonda - -
Time Series with R/Python S&DS 583 Dhafer Malouche Mon, Wed 2:30-3:45 24 Hillhouse
Selected Topics in Statistical Decision Theory S&DS 411a/611a Harrison Zhou Wed 9:00 - 11:15 24 Hillhouse
Statistical Case Studies S&DS 625a Jay Emerson Mon, Wed 1:00-2:15 WTS A74
Equity in AI Systems S&DS 650a Elisa Celis Tues 1:30-3:20 24 Hillhouse
Computational Mathematics for Data Science S&DS 663a Roy Lederman Mon, Wed 11:35-12:50 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 - -
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 4:00-5:30 24 Hillhouse
Introductory Statistics S&DS 100b/500b Ethan Meyers Tues, Thurs 9:00-10:15 TBD
YaleData S&DS 123b Jessi Cisewski Mon, Wed, Fri 10:30-11:20 Luce 101
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 WTS A74
YData: Text Data Science: An Introduction S&DS 171b/571b Derek Feng Thurs 9:25-11:15 LC 208
YData: Data Science for Political Campaigns S&DS 172b/572b PLSC347b/524b Joshua Kalla Thurs 9:25-11:15 LC 104
YData: Analysis of Baseball Data S&DS 173b/573b Ethan Meyers Wed 1:30-3:20 TBD
Intensive Introductory Statistics and Data Science S&DS 220b/520b Joe Chang Tues, Thurs 9:00-10:15 WLH 119
Data Exploration and Analysis S&DS 230b/530b PLSC 530b Jonathan Reuning-Scherer Tues, Thurs 9:00-10:15 Davies Aud.
Probability for Data Science S&DS 240b/540b Harrison Zhou Mon, Wed 1:00-2:15 TBD
Theory of Statistics S&DS 242b/542b Zhou Fan Mon, Wed 9:00-10:15 Davies
Computational Tools for Data Science S&DS 262b/562b Roy Lederman Mon, Wed 11:35-12:50 TBD
Introduction to Causal Inference S&DS 314b Winston Lin Tues, Thurs 4:00-5:15 TBD
Stochastic Processes S&DS 351b/551b Amin Karbasi Mon, Wed 1:00-2:15 WLH 119
Data Analysis S&DS 361b/661b Joe Chang Mon, Wed 2:30-3:45 ML 211
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 LC 101
Advanced Probability S&DS 400b/600b MATH 330b Sekhar Tatikonda Tues, Thurs 2:30-3:45 ML 211
Senior Capstone: Statistical Case Studies S&DS 425b Jay Emerson Mon, Wed 2:30-3:45 17 Hillhouse Room 101 (TEAL)
Senior Seminar and Project S&DS 490b TBD or not offered TBD 24 Hillhouse Room 107
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
Computation and Optimization S&DS 631 Dan Spielman Tues, Thurs 1:00-2:15 TBD
Theory of Deep Learning S&DS 670a Andrew Barron Mon, Wed 9:00-10:15 24 Hillhouse
Applied Spatial Statistics S&DS 674b/F&ES 781b Tim Gregoire Tues, Thurs 10:30-11:50 KRN G01
An Introduction to R for Statistical Computing and Data Science (1/2 credit) S&DS 150/510a
not taught this year
Statistics and Data Science Computing Laboratory (1/2 credit) S&DS 110b/510b
not taught this year
Data Science Ethics S&DS 150b
not taught this year
YData: ExoStatistics: Exploring Extrasolar Planets with Data Science S&DS 170b570b
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
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 508b
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
Statistical Computing S&DS 662b
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
Statistical Learning Theory S&DS 669b
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
Signal Processing for Data Science S&DS 676b
not taught this year
High-Dimensional Statistical Estimation S&DS 679a
not taught this year
Statistical Methods in Neuroimaging S&DS 683a
not taught this year
Statistical Inference on Graphs S&DS 684a
not taught this year
Placeholder -- Monograph 706
not taught this year

Introductory Statistics (S&DS 100b/500b)
Instructor: Ethan Meyers
Time: Tues, Thurs 9:00-10:15
Place: TBD
An introduction to statistical reasoning. Topics include numerical and graphical summaries of data, data acquisition and experimental design, probability, hypothesis testing, confidence intervals, correlation and regression. Application of statistical concepts to data; analysis of real-world problems. A faster-paced version of this course with a higher level of computing is being created: See STAT 220a.
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Introduction to Statistics (S&DS 101a-109a/501a-509a)
Instructor: Jonathan Reuning-Scherer and Staff
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Webpage:  http://www.stat.yale.edu/Courses/QR/stat101106.html
A basic introduction to statistics, including numerical and graphical summaries of data, probability, hypothesis testing, confidence intervals, and regression. Each course focuses on applications to a particular field of study and is taught jointly by two instructors, one specializing in statistics and the other in the relevant area of application. The first seven weeks of classes are attended by all students in STAT 101-106 together, as general concepts and methods of statistics are developed. The remaining weeks are divided into field-specific sections that develop the concepts with examples and applications. Computers are used for data analysis. These courses are alternatives; they do not form a sequence and only one may be taken for credit. No prerequisites beyond high school algebra. May not be taken after STAT 100 or 109.

Students enrolled in STAT 101-106 who wish to change to STAT 109, or those enrolled in STAT 109 who wish to change to STAT 101-106, must submit a course change notice, signed by the instructor, to their residential college dean by Friday, September 28. The approval of the Committee on Honors and Academic Standing is not required.

NEW for Fall 2019: S&DS 108: Introduction to Statistics: Advanced Fundamentals is available and allows students to earn 1/2 credit for completing one of the field-specific courses during the second half of the semester.
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Introduction to Statistics: Life Sciences (S&DS 101a/501a E&EB 210aG/MCDB 215a)
Instructor: Jonathan Reuning-Scherer and Walter Jetz
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical and probabilistic analysis of biological problems presented with a unified foundation in basic statistical theory. Problems are drawn from genetics, ecology, epidemiology, and bioinformatics.
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Introduction to Statistics: Political Science (S&DS 102a/502a EP&E 203a/PLSC 425a)
Instructor: Jonathan Reuning-Scherer and Kelly Rader
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical analysis of politics and quantitative assessments of public policies. Problems presented with reference to a wide array of examples: public opinion, campaign finance, racially motivated crime, and health policy.
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Introduction to Statistics: Social Sciences (S&DS 103a/503a SOCY 119a)
Instructor: Jonathan Reuning-Scherer and Ethan Meyers
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Descriptive and inferential statistics applied to analysis of data from the social sciences. Introduction of concepts and skills for understanding and conducting quantitative research.
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Introduction to Statistics: Medicine (S&DS 105a/505a)
Instructor: Jonathan Reuning-Scherer and Russell Barber
Time: Tues, Thurs 1:00-2:15
Place: OML 202
Statistical methods used in medicine and medical research. Practice in reading medical literature competently and critically, as well as practical experience performing statistical analysis of medical data.
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Introduction to Statistics: Data Analysis (S&DS 106a/506a)
Instructor: Jonathan Reuning-Scherer and David Brinda
Time: Tues, Thurs 1:00-2:15
Place: 
An introduction to Probability and Statistics with emphasis on data analysis.
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Introduction to Statistics: Advanced Fundamentals (S&DS 108a)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: TBD
More advanced concepts and methods in statistics. Meets for the second half of the term only. May not be taken after STAT 100 or after completing 101-106 or after more advanced coursework.
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Introduction to Statistics: Fundamentals (S&DS 109a)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: OML 202
General concepts and methods in statistics. Meets for the first half of the term only. May not be taken after STAT 100 or 101-106.
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[ An Introduction to R for Statistical Computing and Data Science (1/2 credit) (S&DS 150/510a) ]
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[ Statistics and Data Science Computing Laboratory (1/2 credit) (S&DS 110b/510b) ]
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YaleData (S&DS 123b)
Instructor: Jessi Cisewski
Time: Mon, Wed, Fri 10:30-11:20
Place: Luce 101
Computational, programming, and statistical skills are no longer optional in our increasingly data-driven world; these skills are essential for opening doors to manifold research and career opportunities. This course aims to dramatically enhance knowledge and capabilities in fundamental ideas and skills in data science, especially computational and programming skills along with inferential thinking. YData is an introduction to Data Science that emphasizes the development of these skills while providing opportunities for hands-on experience and practice. YData is accessible to students with little or no background in computing, programming, or statistics, but is also engaging for more technically oriented students through extensive use of examples and hands-on data analysis. Python 3, a popular and widely used computing language, is the language used in this course. The computing materials will be hosted on a special purpose web server.
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Foreign Assistance to Sub-Saharan Africa: Archival Data Analysis (S&DS 138b/AFST 378/EVST 378/AFST 570)
Instructor: Russell Barbour
Time: Tues, Thurs 2:30-3:45
Place: WTS A74
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[ Data Science Ethics (S&DS 150b) ]
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[ YData: ExoStatistics: Exploring Extrasolar Planets with Data Science (S&DS 170b570b) ]
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YData: Text Data Science: An Introduction (S&DS 171b/571b)
Instructor: Derek Feng
Time: Thurs 9:25-11:15
Place: LC 208
Written language is the primary means by which humans document their observations of the world, including scientific discoveries, interpretations of history and art, health diagnoses, analyses of political events and economic trends, social interactions, and many others. Increasingly, this rapidly growing transcript is readily available in electronic form, and is being used in commercial applications and to advance scientific knowledge. Text Data Science is an introduction to computational and inferential methods that use text. The focus is on simple but often powerful text processing techniques that do not require linguistic analyses, to gain familiarity with working with text data. Sources used in the seminar include political speeches, Twitter feeds, scientific journals, online FAQ and discussion boards, Wikipedia, news articles, and consumer product reviews. Methodologies include scraping, wrangling, hashing, sorting, regressing, embedding, and probabilistic modeling. The course is based on the Python programming language within a cloud computing platform, and is paced to be accessible to students who have previously taken or are currently enrolled in YData (S&DS 123). Prerequisite: S&DS 123, which may be taken concurrently. 0.5 Yale College course credit(s)
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YData: Data Science for Political Campaigns (S&DS 172b/572b PLSC347b/524b)
Instructor: Joshua Kalla
Time: Thurs 9:25-11:15
Place: LC 104
Political campaigns have become increasingly data driven. Data science is used to inform where campaigns compete, which messages they use, how they deliver them, and among which voters. In this course, we explore how data science is being used to design winning campaigns. Students gain an understanding of what data is available to campaigns, how campaigns use this data to identify supporters, and the use of experiments in campaigns. This course provides students with an introduction to political campaigns, an introduction to data science tools necessary for studying politics, and opportunities to practice the data science skills presented in S&DS 123, YData. Prerequisite: S&DS 123, which may be taken concurrently. 0.5 Yale College course credit(s)
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YData: Analysis of Baseball Data (S&DS 173b/573b)
Instructor: Ethan Meyers
Time: Wed 1:30-3:20
Place: TBD
Description needed, brainstorming. Prerequisite: S&DS 123, which may be taken concurrently. 0.5 Yale College course credit(s)
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Intensive Introductory Statistics and Data Science (S&DS 220b/520b)
Instructor: Joe Chang
Time: Tues, Thurs 9:00-10:15
Place: WLH 119
Introduction to statistical reasoning for students with particular interest in data science and computing. Using the R language, topics include exploratory data analysis, probability, hypothesis testing, confidence intervals, regression, statistical modeling, and simulation. Computing taught and used extensively, as well as application of statistical concepts to analysis of real-world data science problems. MATH 115 is helpful, but not required.
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Data Exploration and Analysis (S&DS 230a/530a PLSC 530a)
Instructor: Ethan Meyers
Time: Tues, Thurs 9:00-10:15
Place: DL 220
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used. After STAT 100 or the equivalent or with permission from the instructor; students without prior coursework in statistics should take STAT 100, 10X, or 200.
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Data Exploration and Analysis (S&DS 230b/530b PLSC 530b)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 9:00-10:15
Place: Davies Aud.
Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. The R computing language and Web data sources are used. After STAT 100 or the equivalent or with permission from the instructor; students from STAT 200 may be permitted in 230 but are encouraged to take 361 and/or 325.
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(Bayesian) Probability and Statistics (S&DS 238a/538a)
Instructor: Joe Chang
Time: Tues, Thurs 1:00-2:15
Place: ML 211
Fundamental principles and techniques of probabilistic thinking, statistical modeling, and data analysis. Essentials of probability, including conditional probability, random variables, distributions, law of large numbers, central limit theorem, and Markov chains. Statistical inference with emphasis on the Bayesian approach: parameter estimation, likelihood, prior and posterior distributions, Bayesian inference using Markov chain Monte Carlo. Introduction to regression and linear models. Computers are used for calculations, simulations, and analysis of data.

Prerequisite: knowledge of single variable calculus is assumed. Some brief acquaintance with multivariable calculus (e.g. double integrals) and matrices would also be helpful but are not required.
Extra: STAT 238 Extra Session,  Tues 6:30-8:00,  24 Hillhouse
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[ Theory of Probability and Statistics (S&DS 239a/539a) ]
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Probability for Data Science (S&DS 240b/540b)
Instructor: Harrison Zhou
Time: Mon, Wed 1:00-2:15
Place: TBD
Introduction to probability theory, not for the major.
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Probability Theory with Applications (S&DS 241a/541a MATH 241a)
Instructor: Winston Lin
Time: Mon, Wed 9:00-10:15
Place: SSS 114
Introduction to probability theory. Topics include probability spaces, random variables, expectations and probabilities, conditional probability, independence, discrete and continuous distributions, central limit theorem, Markov chains, and probabilistic modeling.
Extra: STAT 241 TA Session,  Thurs 6:30-7:30,  24 Hillhouse
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Theory of Statistics (S&DS 242b/542b)
Instructor: Zhou Fan
Time: Mon, Wed 9:00-10:15
Place: Davies
Study of the principles of statistical analysis. Topics include maximum likelihood, sampling distributions, estimation, confidence intervals, tests of significance, regression, analysis of variance, and the method of least squares. Some statistical computing.
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Computational Tools for Data Science (S&DS 262b/562b)
Instructor: Roy Lederman
Time: Mon, Wed 11:35-12:50
Place: TBD
Assumes math chops and some type of programming.
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Linear Models (S&DS 312a/612a)
Instructor: David Brinda
Time: Mon, Wed 11:35-12:50
Place: WLH 116
The geometry of least squares; distribution theory for normal errors; regression, analysis of variance, and designed experiments; numerical algorithms, with particular reference to the R statistical language.

After STAT 242 and MATH 222 or 225.

No final exam.
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Introduction to Causal Inference (S&DS 314b)
Instructor: Winston Lin
Time: Tues, Thurs 4:00-5:15
Place: TBD
Introduction to causal inference with applications to the social and health sciences. Topics include randomized experiments, matching and propensity score methods, sensitivity analysis, instrumental variables, and regression discontinuity designs. Mathematical problems, data analysis in R, and critical discussions of published applied research.

Prerequisite: S&DS 242 and some programming experience in R.
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Measuring Impact and Opinion Change (S&DS 315a/PLSC 340a)
Instructor: Josh Kalla
Time: Tues, Thurs 2:30-3:45
Place: TBD
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Stochastic Processes (S&DS 351b/551b)
Instructor: Amin Karbasi
Time:  Mon, Wed 1:00-2:15
Place: WLH 119
Introduction to the study of random processes, including Markov chains, Markov random fields, martingales, random walks, Brownian motion, and diffusions. Techniques in probability, such as coupling and large deviations. Applications chosen from image reconstruction, Bayesian statistics, finance, probabilistic analysis of algorithms, and genetics and evolution.
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Introduction to Data Mining and Machine Learning (S&DS 355a/555a)
Instructor: John Lafferty
Time: Tues, Thurs 9:00-10:15
Place: OML 202
BRAINSTORMING: Fewer mathematical prerequisites, for the certificate and not for the major?
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Data Analysis (S&DS 361b/661b)
Instructor: Joe Chang
Time: Mon, Wed 2:30-3:45
Place: ML 211
Selected topics in statistics explored through analysis of data sets using the R statistical computing language. Topics include linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering.

After or concurrently with STAT 242 and MATH 222 or 225, or equivalents.
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Multivariate Statistics for Social Sciences (S&DS 363b/563b)
Instructor: Jonathan Reuning-Scherer
Time: Tues, Thurs 1:00-2:15
Place: KRN 301
Introduction to the analysis of multivariate data as applied to examples from the social sciences. Topics include principal components analysis, factor analysis, cluster analysis (hierarchical clustering, k-means), discriminant analysis, multidimensional scaling, and structural equations modeling. Extensive computer work using either SAS or SPSS programming software.

Prerequisites: knowledge of basic inferential procedures and experience with linear models.
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Information Theory (S&DS 364b/664b)
Instructor: Andrew Barron
Time: Tues, Thurs 11:35-12:50
Place: 24 Hillhouse
Foundations of information theory in mathematical communications, statistical inference, statistical mechanics, probability, and algorithmic complexity. Quantities of information and their properties: entropy, conditional entropy, divergence, redundancy, mutual information, channel capacity. Basic theorems of data compression, data summarization, and channel coding. Applications in statistics and finance. After Statistics 241.
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Applied Data Mining and Machine Learning (S&DS 365a/565a)
Instructor: Derek Feng
Time: Tues, Thurs 9:00-10:15
Place: LC 101
Techniques for data mining and machine learning are covered from both a statistical and a computational perspective, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. The course will give the basic ideas and intuition behind these methods, a more formal understanding of how and why they work, and opportunities to experiment with machine learning algorithms and apply them to data. After STAT 242b.
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Applied Data Mining and Machine Learning (S&DS 365b/665b)
Instructor: Derek Feng
Time: Mon, Wed 11:35-12:50
Place: LC 101
Techniques for data mining and machine learning are covered from both a statistical and a computational perspective, including support vector machines, bagging, boosting, neural networks, and other nonlinear and nonparametric regression methods. The course will give the basic ideas and intuition behind these methods, a more formal understanding of how and why they work, and opportunities to experiment with machine learning algorithms and apply them to data. After STAT 242b.
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[ Design and Analysis of Algorithms (CPSC 365b) ]
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Advanced Probability (S&DS 400b/600b MATH 330b)
Instructor: Sekhar Tatikonda
Time: Tues, Thurs 2:30-3:45
Place: ML 211
Measure theoretic probability, conditioning, laws of large numbers, convergence in distribution, characteristic functions, central limit theorems, martingales. Some knowledge of real analysis is assumed.
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Statistical Inference (S&DS 410a/610a)
Instructor: Zhou Fan
Time: Tues, Thurs 11:35-12:50
Place: WTS B52
A systematic development of the mathematical theory of statistical inference covering methods of estimation, hypothesis testing, and confidence intervals. An introduction to statistical decision theory. Undergraduate probability at the level of Statistics 241a assumed.
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Senior Capstone: Statistical Case Studies (S&DS 425b)
Instructor: Jay Emerson
Time: Mon, Wed 2:30-3:45
Place: 17 Hillhouse Room 101 (TEAL)
Webpage:  https://classesv2.yale.edu/
Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R. This is a senior seminar of limited size, but other students may join if space permits. A final project is required. S&DS or Applied Math majors who previously took Statistical Case Studies are not permitted to take this course.
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Optimization Techniques (S&DS 430a/630a ENAS 530a EENG 437a ECON 413a)
Instructor: Sekhar Tatikonda
Time: Tues, Thurs 1:00-2:15
Place: WTS A51
Fundamental theory and algorithms of optimization, emphasizing convex optimization. The geometry of convex sets, basic convex analysis, the principle of optimality, duality. Numerical algorithms: steepest descent, Newton's method, interior point methods, dynamic programming, unimodal search. Applications from engineering and the sciences.
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Indep Study (S&DS 480ab)
Instructor: Staff
Time: -
Place: -
Directed individual study for qualified students who wish to investigate an area of statistics not covered in regular courses. A student must be sponsored by a faculty member who sets the requirements and meets regularly with the student. Enrollment requires a written plan of study approved by the faculty adviser and the director of undergraduate studies.

Permission required. No final Exam.
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[ Senior Seminar and Project (S&DS 490a) ]
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Senior Seminar and Project (S&DS 490b)
Instructor: TBD or not offered
Time: TBD
Place: 24 Hillhouse Room 107
Under the supervision of a member of the faculty, each student works on an independent project. Students participate in seminar meetings at which they speak on the progress of their projects.

Permission required. No final Exam.
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Senior Project (S&DS 491a)
Instructor: Sekhar Tatikonda
Time: -
Place: -
Individual research that fulfills the S&DS senior requirement. Requires a faculty adviser and DUS permission. The student must submit a written report about results of the project.
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[ Senior Project (S&DS 492b) ]
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[ Research Design and Causal Inference (PLSC 508b) ]
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[ Applied Linear Models (S&DS 531a) ]
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[ Intensive Algorithms (S&DS 566) ]
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Topics in Deep Learning: Methods and Biomedical Applications (S&DS 567 CB&B 567)
Instructor: Martin Renqiang and Mark Gerstein
Time: Mon 9:00-11:15
Place: TBD
This course provides an introduction to recent developments in deep learning, covering topics ranging from basic backpropagation, optimization, to latest developments in deep generative models and network robustness. Applications in Natural Language Processing and Computer Vision will be used as running examples. Several case studies in biomedical applications will be covered in details.
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Time Series with R/Python (S&DS 583)
Instructor: Dhafer Malouche
Time: Mon, Wed 2:30-3:45
Place: 24 Hillhouse
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Selected Topics in Statistical Decision Theory (S&DS 411a/611a)
Instructor: Harrison Zhou
Time: Wed 9:00 - 11:15
Place: 24 Hillhouse
In this course we will review some recent developments in statistical decision theory including nonparametric estimation, high dimensional (non)linear estimation, low rank and sparse matrices estimation, covariance matrices estimation, graphical models, and network analysis.
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[ Introduction to Random Matrix Theory and Applications (S&DS 615b) ]
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Statistical Case Studies (S&DS 625a)
Instructor: Jay Emerson
Time: Mon, Wed 1:00-2:15
Place: WTS A74
Webpage:  https://classesv2.yale.edu/
Statistical analysis of a variety of statistical problems using real data. Emphasis on methods of choosing data, acquiring data, assessing data quality, and the issues posed by extremely large data sets. Extensive computations using R. Limited size, with permission from the instructor required. STARRED? STAT 425 is a senior capstone version of this course that include a final project. Can both be taken? Probably not.
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Practical Work (S&DS 626ab)
Instructor: DGS
Time: -
Place: -
Individual one-semester projects, with students working on studies outside the Department, under the guidance of a statistician. This course is a one-credit requirement for the Ph.D. degree.
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Statistical Consulting (S&DS 627a/628b)
Instructor: Jay Emerson
Time: Fri 2:30-4:30
Place: 24 Hillhouse
Webpage:  http://www.stat.yale.edu/~jay/627.html
Statistical consulting and collaborative research projects often require statisticians to explore new topics outside their area of expertise. This course exposes students to real problems, requiring them to draw on their expertise in probability, statistics, and data analysis. Students complete the course with individual projects supervised jointly by faculty outside the department and by one of the instructors. Students enroll for both terms and receive one credit at the end of the year.
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Computation and Optimization (S&DS 631)
Instructor: Dan Spielman
Time: Tues, Thurs 1:00-2:15
Place: TBD
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Statistical Methods in Human Genetics (S&DS 645b/BIS 692b)
Instructor: Hongyu Zhao
Time: TBD
Place: TBD
Probability modeling and statistical methodology for the analysis of human genetics data are presented. Topics include population genetics, single locus and polygenic inheritance, linkage analysis, genome-wide association studies, quantitative trait locus analysis, rare variant analysis, and genetic risk predictions. Offered every other year. Prerequisite: EPH 505 and BIS 505, or equivalents, and permission from the instructor.
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Equity in AI Systems (S&DS 650a)
Instructor: Elisa Celis
Time: Tues 1:30-3:20
Place: 24 Hillhouse
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[ Statistical Computing (S&DS 662b) ]
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[ Spectral Graph Theory (CPSC 662a) ]
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Computational Mathematics for Data Science (S&DS 663a)
Instructor: Roy Lederman
Time: Mon, Wed 11:35-12:50
Place: 24 Hillhouse
The course explores the mechanics of the interface between mathematics, computation and statistics in data analysis. We will discuss topics in numerical computation, complexity, programming and prototyping. Assignments will include theory, programming, data analysis, individual work, collaborative work and making mistakes.

Prerequisites: Linear algebra and some experience with programming (any language).
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[ Probabilistic Networks, Algorithms, and Applications (S&DS 667a) ]
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[ Nonparametric Estimation and Machine Learning (S&DS 468b) ]
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[ Statistical Learning Theory (S&DS 669b) ]
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Theory of Deep Learning (S&DS 670a)
Instructor: Andrew Barron
Time: Mon, Wed 9:00-10:15
Place: 24 Hillhouse
Deep neural networks and related statistical learning theory are developed for high-dimensional function estimation and classification. Complexity, approximation capability, statistical accuracy, penalized least squares and stochastic optimization are explored. Students will be expected to propose a topic of investigation (e.g. of literature or computational or theoretical exploration of discussed methods) and to provide a final report. This course is intended for students with background in probability, statistics, and computation.
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[ Topics on Random Graphs (MATH 670) ]
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[ Information Theory Tools in Probability and Statistics (S&DS 672a) ]
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Applied Spatial Statistics (S&DS 674b/F&ES 781b)
Instructor: Tim Gregoire
Time: Tues, Thurs 10:30-11:50
Place: KRN G01
An introduction to spatial statistical techniques with computer applications. Topics include spatial sampling, visualizing spatial data, quantifying spatial association and autocorrelation, interpolation methods, fitting variograms, kriging, and related modeling techniques for spatially correlated data. Examples are drawn from ecology, sociology, public health, and subjects proposed by students. Four to five lab/homework assignments and a final project. The class makes extensive use of the R programming language as well as ArcGIS.
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[ Signal Processing for Data Science (S&DS 676b) ]
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[ High-Dimensional Statistical Estimation (S&DS 679a) ]
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[ Statistical Methods in Neuroimaging (S&DS 683a) ]
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[ Statistical Inference on Graphs (S&DS 684a) ]
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Independent Study or Topics Course (S&DS 690ab)
Instructor: DGS
Time: -
Place: -
By arrangement with faculty. Approval of Director of Graduate Studies required.
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Research Seminar in Probability (S&DS 699ab)
Instructor: Sekhar Tatikonda and David Pollard
Time: Fri 11:00-1:00
Place: 24 Hillhouse
Webpage:  http://www.stat.yale.edu/~ypng
Continuation of the Yale Probability Network Group seminar. Student and faculty explanations of current research in areas such as random graph theory, spectral graph theory, Markov chains on graphs, and the objective method.

Credit only with the explicit permission of the seminar organizers.
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Departmental Seminar (S&DS 700ab)
Instructor: -
Time: Mon 4:00-5:30
Place: 24 Hillhouse
Webpage:  http://www.stat.yale.edu/Seminars/2011-12/
Important activity for all members of the department. See webpage for weekly seminar announcements.
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