Department of Statistics and Data Science
Working Course List for Fall 2021/Spring 2022

• Organizational Meeting for all SPRING term courses whose times are not listed below: TBD (see email or contact john DOT emerson AT yale.edu.
• Those interested in attending one of the courses but unable to be present at this meeting should inform Jay Emerson beforehand and submit their schedules.
• Courses numbered 600 or above (such as Stat 610a) are intended primarily for graduate students. If such a course does not have an undergraduate cross-listing, undergraduates should consult with the instructor.
• Tables of class times: FALL and SPRING
• Course lists for other years:
     1994-95    1995-96    1996-97    1997-98    1998-99    1999-00    2000-01    2001-02    2002-03    2003-04    2004-05    2005-06    2006-07    2007-08    2008-09    2009-10    2010-11    2011-12    2012-13    2013-14    2014-15    2015-16    2016-17    2017-18    2018-19    2019-20    2020-21    [this year]    2022-23    2023-24    2024-25 .
  These pages are not being updated; some of the links may no longer be working.
• Director of Undergraduate Studies: Sekhar Tatikonda
• Director of Graduate Studies: John Emerson and Andrew Barron

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 101-109/501-509)

Instructor: Jonathan Reuning-Scherer and Staff
Time: Tues, Thurs 1:00-2:15
Place: YSB MARSH
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
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
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 Ethan Meyers
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 Brian MacDonald
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 110/510b)

Instructor: John Emerson
Time: Mon, Wed 11:45-12:50
Place: TBD
This is a 1/2 credit lab-like course; it will meet for the first seven weeks of the term (with the last week being the week starting March 15). The class provides an introduction to the R statistical language, based on the S language developed at Bell Labs by John Chambers and Richard Becker. It has become the accepted language for advanced statistical computing and data sciencei in both industry and a wide range of academic disciplines. It is intended for students with no programming experience in any language.

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[ Statistics and Data Science Computing Laboratory (1/2 credit) (S&DS 110b/510b) ]

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An Introduction to R? for Statistical Computing and Data Science (1 credit?) (S&DS 110/510a)

Instructor: Elena Khusainova
Time: TBD
Place: TBD
This is a 1/2 credit course that meets for the first 7 weeks of the semester. The class provides an introduction to the R statistical language, based on the S language developed at Bell Labs by John Chambers and Richard Becker. It has become the accepted language for advanced statistical computing and data sciencei in both industry and a wide range of academic disciplines.

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YData: An Introduction to Data Science (S&DS 123b)

Instructor: Ethan Meyers
Time: Mon, Wed, Fri 10:30-11:20
Place: TBD
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: TBD

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Data Science Ethics (S&DS 150b)

Instructor: Elisa Celis
Time: Tues, Thurs 1:00-2:15
Place: TBD
Needed.

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YData: Text Data Science: An Introduction (S&DS 171b/571b)

Instructor: Ethan Meyers (bracket?)
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: Wed 1:30-3:20
Place: TBD
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 (bracket)
Time: Wed 1:30-3:20
Place: TBD
Prerequisite: S&DS 123, which may be taken concurrently.

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YData: Statistics in the Media (S&DS 174b/574b)

Instructor: A. Donoghue (?)
Time: Wed 9:25-11:15
Place: TBD
Prerequisite: S&DS 123, which may be taken concurrently.

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YData: Measuring Culture (S&DS 175b/575b)

Instructor: Daniel Karell
Time: Thurs 3:30-5:20
Place: TBD
Prerequisite: S&DS 123, which may be taken concurrently.

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YData: Humanities Data Mining (S&DS 176b/576b)

Instructor: Peter Leonard
Time: Tues, Thurs 1:00-2:15
Place: TBD
Prerequisite: S&DS 123, which may be taken concurrently.

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YData: COVID-19 Behavior (S&DS 177b/577b)

Instructor: Youpei Yan (?)
Time: Thurs 9:25-11:15
Place: TBD
Prerequisite: S&DS 123, which may be taken concurrently.

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Intensive Introductory Statistics and Data Science (S&DS 220b/520b)

Instructor: Brian MacDonald
Time: Tues, Thurs 9:00-10:15
Place: TBD
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 230/530 PLSC 530)

Instructor: Ethan Meyers
Time: Tues, Thurs 9:00-10:15
Place: ML 211
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: TBD
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 238/538)

Instructor: Joe Chang
Time: Tues, Thurs 1:00-2:15
Place: 17HLH 101 - TEAL
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 240/540)

Instructor: Elisa Celis
Time: Mon, Wed 2:30-3:45
Place: ML 211
Introduction to probability theory, not for the major.

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Probability Theory with Applications (S&DS 241/541 MATH 241)

Instructor: Yihong Wu
Time: Mon, Wed 9:00-10:15
Place: DAVIES AUD
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: TBD
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 262/562)

Instructor: Roy Lederman
Time: Mon, Wed 1:00-2:15
Place: DL 220
Assumes math chops and some type of programming.

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Introductory Machine Learning (S&DS 265/565)

Instructor: John Lafferty
Time: Tues, Thurs 9:00-10:15
Place: WLH 201
BRAINSTORMING: Fewer mathematical prerequisites, for the certificate and not for the major?

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Linear Models (S&DS 312/612)

Instructor: David Brinda
Time: Mon, Wed 11:35-12:50
Place: DL 220
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 (on Leave?)
Time: Tues, Thurs 4:00-5:15
Place: TBD

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Topics in the Design and Analysis of Experiments (S&DS 316a/516a)

Instructor: Winston Lin
Time: 
Place: 

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Applied Machine Learning and Causal Inference (S&DS 317b/517b)

Instructor: Jas Sekhon
Time: Tues, Thurs 4:00-5:15
Place: TBD
Undergrad and graduate number needed.

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Stochastic Processes (S&DS 351b/551b)

Instructor: Andrew Barron
Time:  Mon, Wed 1:00-2:15
Place: TBD
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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Biomedical Data Science, Mining and Modeling (S&DS 352/MCDB 452)

Instructor: Mark Gerstein and Matthew Simon
Time: Mon, Wed 1:00-2:15
Place: 

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Data Analysis (S&DS 361b/661b)

Instructor: Elena Khusainova
Time: Tues, Thurs 9:00-10:15
Place: TBD
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: TBD
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: Yihong Wu
Time: Tues, Thurs 11:35-12:50
Place: TBD
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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Intermediate Machine Learning (S&DS 365a/665a)

Instructor: John Lafferty
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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Applied Data Mining and Machine Learning (S&DS 365b/665b)

Instructor: Sahand Negahban
Time: Mon, Wed 11:35-12:50
Place: TBD
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 400/600 MATH 330)

Instructor: Sekhar Tatikonda
Time: Tues, Thurs 2:30-3:45
Place: WTS A51
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 410/610)

Instructor: Zhou Fan
Time: Tues, Thurs 11:35-12:50
Place: LUCE 202
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: Brian MacDonald
Time: Tues, Thurs 2:30-3:45
Place: TBD
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. 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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Statistical Case Studies (S&DS 425)

Instructor: Brian MacDonald
Time: Mon, Wed 2:30-3:45
Place: 17HLH 111
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 seminar of limited size and is not for capstone credit.

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[ Optimization Techniques (S&DS 430a/630a ENAS 530a EENG 437a ECON 413a) ]

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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 491)

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 508a) ]

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Design-Based Inference for the Social Sciences (PLSC 528b)

Instructor: Peter Aronow
Time: Mon 3:30-5:20
Place: TBD
Introduction to design-based statistical approaches to survey sampling and causal inference. Design and analysis of complex survey samples and randomized experiments, including model-assisted approaches. Discussion of recent advances in this paradigm, including inference in network settings. Prerequisite: knowledge of statistical theory at the level of PLSC 500 is assumed, with familiarity with probability and estimation theory. Alternative prerequisite courses include S&DS 542 or ECON 550.

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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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Selected Topics in Statistical Decision Theory (S&DS 411a/611b)

Instructor: Harrison Zhou
Time: Tues 3:30-5:20
Place: TBD
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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Applied Machine Learning and Causal Inference Research Seminar (S&DS 617)

Instructor: Jas Sekhon
Time: Wed 4:00-5:50
Place: RKZ 06
Research seminar, graduate number needed.

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Statistical Case Studies (S&DS 625)

Instructor: Jay Emerson
Time: Mon, Wed 2:30-3:45
Place: 17HLH 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. Limited size, with permission from the instructor required. This course is likely limited to graduate students in S&DS; undergraduate version 425 is available.

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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 431/631)

Instructor: Anna Gilbert
Time: Tues, Thurs 1:00-2:15
Place: WTS A60

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Advanced Optimization Techniques (S&DS 432b/632b)

Instructor: Sekhar Tatikonda
Time: Tues, Thurs 1:00-2:15
Place: TBD

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Statistical Methods in Human Genetics (S&DS 645b/BIS 692b/CB&B 692)

Instructor: Hongyu Zhao?
Time: Thurs 1:00-2:50
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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Markov chains for sampling and optimization (S&DS 652a)

Instructor: Andrew Barron
Time: Tues, Thurs 1:00 - 2:15
Place: 

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Statistical Computing (S&DS 662)

Instructor: Jay Emerson
Time: Mon, Wed 9:00-10:15
Place: 17HLH 101 - TEAL
Topics in the practice of data analysis and statistical computing, with particular attention to problems involving massive data sets or large, complex simulations and computations. Progamming with R, C/C++, and Python, computational efficiency, memory management, interactive and dynamic graphics, and parallel computing.

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[ Spectral Graph Theory (CPSC 662a) ]

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Sum-of-Squares Optimization (CPSC 663)

Instructor: TBA
Time: Mon, Wed 1:00-2:15
Place: WTS A68
This course is a survey of sum-of-squares (SOS) polynomial proofs and their applications in and connections to various fields of mathematics and computer science. SOS proofs try to bound polynomial optimization problems or show that polynomial systems of equations cannot be solved by using the simple-looking fact that squared polynomials are non-negative. We first review the long mathematical history of SOS proofs, including Hilbert’s “Nullstellensatz” and 17th problem, classical moment problems, and more recent “Positivstellensatz” results. After this overview, the main focus of the course is on recent developments relating SOS proofs to questions in optimization and computer science. We see how to search for SOS proofs using semidefinite programming, and how such algorithms have yielded theoretical breakthroughs for many problems of current interest. Having seen the power of SOS algorithms, we also look at lower bounds against these algorithms, which give some of the strongest evidence we have of computational hardness, especially for high-dimensional random problems coming from combinatorial optimization, statistics, and machine learning. Depending on student interest, we may reallocate some time to real-world applications of SOS, to practical questions about implementing SOS algorithms, or to using SOS proofs as mathematical proof assistants. One of our main goals is to reach many interesting open problems and to cover enough background to make them accessible to graduate students. Prerequisites: a strong background in linear algebra and probability theory and some experience with convex optimization. Students should have successfully passed a rigorous algorithms course at the level of CPSC 366 and an optimization course such as AMTH 437, CPSC 463, or SDS 631. Open to undergraduates. Prerequisites are strictly enforced, and permission of the instructor is required for all students.

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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 669a)

Instructor: Sahand Negahban?
Time: TBD
Place: 24 Hillhouse
Introduction to theoretical analysis of machine learning algorithms. Focus on the statistical and computational aspects. Will cover subjects such as decision theory, empirical process theory, and convex optimization. Prerequisites linear algebra, multivariable calculus, stochastic processes, and introduction to machine learning such as Stat 365b or a similar course.

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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: TBD
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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[ Topological Data Analysis (S&DS 675a) ]

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[ Signal Processing for Data Science (S&DS 676b) ]

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Information-theoretic methods in high-dimensional statistics (S&DS 677b)

Instructor: Andrew Barron or bracketed?
Time: Tues 3:30-5:20
Place: TBD
The interplay between information theory and statistics is a constant theme in the development of both fields. This course will discuss how techniques rooted in information theory play a key role in understanding the fundamental limits of high-dimensional statistical problems in terms of minimax risk and sample complexity. In particular, we will rigorously justify the phenomena of dimensionality reduction by either intrinsic low-dimensionality (sparsity, smoothness, shape, etc) or - the less familiar - extrinsic low-dimensionality (functional estimation). Complementing this objective of understanding the fundamental limits, another significant direction is to develop computationally efficient procedures that attain the statistical optimality, or to understand the lack thereof.

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Function Estimation (S&DS 679)

Instructor: Andrew Barron
Time: Tues, Thurs 9:00 - 10:15
Place: 24 Hillhouse
A study of the problem of estimation of functions from statistical data from computational, methodological and theoretical perspectives. Aspects investigated include linear versus nonlinear modeling, local smoothing versus global projection, kernel machines and Gaussian process priors, basis adaptation and greedy selection, low versus high-dimensionality, shallow versus deep learning, deterministic versus stochastic search, and minimaxity versus individual risk resolvability.

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[ High-Dimensional Function Estimation (prev title) (S&DS 682a) ]

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[ Statistical Methods in Neuroimaging (S&DS 683a) ]

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Statistical Inference on Graphs (S&DS 684b)

Instructor: Yihong Wu (likely bracket?)
Time: TBD
Place: 24 Hillhouse
An emerging research thread in statistics and machine learning deals with finding latent structures from data represented in graphs or matrices. This course will provide an introduction to mathematical and algorithmic tools for studying such problems. We will discuss information-theoretic methods for determining the fundamental limits, as well as methodologies for attaining these limits, including spectral methods, semidefinite programming relaxations, message passing algorithms, etc. Specific topics will include spectral clustering, planted clique and partition problem, sparse PCA, community detection on stochastic block models, statistical-computational tradeoffs.

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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) ]

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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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