Department of Statistics and Data Science
Course List for Fall 2018/Spring 2019

Revised: 17 January 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-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 4:00-5:30 24 Hillhouse
Introductory Statistics S&DS 100b/500b David Brinda Mon, Wed, Fri 10:30-11:20 DL 200
YaleData S&DS 123b Jessi Cisewski and John Lafferty 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
Data Science Ethics S&DS 150b Elisa Celis Tues, Thurs 1:00-2:15 WLH 203
YData: ExoStatistics: Exploring Extrasolar Planets with Data Science S&DS 170b570b Jessi Cisewski Tues 3:30-5:20 WLH 202
YData: Text Data Science: An Introduction S&DS 171b/571b John Lafferty 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
Intensive Introductory Statistics and Data Science S&DS 220b/520b Susan Wang 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.
Theory of Statistics S&DS 242b/542b Andrew Barron Mon, Wed, Fri 9:25-10:15 DL 220
Stochastic Processes S&DS 351b/551b Yihong Wu and Sahand Negahban Mon, Wed 1:00-2:15 WLH 119
Data Analysis S&DS 361b/661b David Brinda 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 Luce 203 (first week)
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 Susan Wang Mon, Wed 2:30-3:45 WTS A74
Senior Project S&DS 492b Sekhar Tatikonda - -
Research Design and Causal Inference PLSC 508b Winston Lin Tues 3:30-5:20 RKZ 08
Introduction to Random Matrix Theory and Applications S&DS 615b Zhou Fan Wed 2:30-5:00 24 Hillhouse
Statistical Methods in Computational Biology S&DS 645b/BIS 692b Hongyu Zhao Thur 1:00-2:50 LEPH 115
Statistical Learning Theory S&DS 669b Sahand Negahban Tues 2:30-5:00 24 Hillhouse
Selected Topics in Neural Nets S&DS 671b Harrison Zhou Wed 9:00-11:15 24 Hillhouse
Applied Spatial Statistics S&DS 674b/F&ES 781b Tim Gregoire Tues, Thurs 10:30-11:50 KRN G01
Signal Processing for Data Science S&DS 676b Roy Lederman Mon, Wed 11:35-12:50 24 Hillhouse
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

Introductory Statistics (S&DS 100b/500b)
Instructor: David Brinda
Time: Mon, Wed, Fri 10:30-11:20
Place: DL 200
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-106a/501a-506a)
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.
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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
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
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: 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: 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 110a/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 and John Lafferty
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)
Instructor: Elisa Celis
Time: Tues, Thurs 1:00-2:15
Place: WLH 203
Needed.
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YData: ExoStatistics: Exploring Extrasolar Planets with Data Science (S&DS 170b570b)
Instructor: Jessi Cisewski
Time: Tues 3:30-5:20
Place: WLH 202
Extrasolar planets, or exoplanets, are planets orbiting stars outside our Solar System. The past decade has lead to a proliferation of exoplanet discoveries using various detection methods. Through the lens of data science, we will investigate exoplanet datasets to learn how to find exoplanets, examine the population properties of observed exoplanets, estimate probabilities of another Earth-like exoplanet in our Universe, and probe other questions about exoplanets. This course will provide students with an introduction to exoplanet astronomy, an introduction to data science tools necessary for studying exoplanets, and opportunities to practice the data science skills presented in YData (S&DS 123/523). This course can be taken concurrently with YData (S&DS 123/523) or after successfully completing YData. 0.5 Yale College course credit(s)
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YData: Text Data Science: An Introduction (S&DS 171b/571b)
Instructor: John Lafferty
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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Intensive Introductory Statistics and Data Science (S&DS 220b/520b)
Instructor: Susan Wang
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: Susan Wang
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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Probability Theory with Applications (S&DS 241a/541a MATH 241a)
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: Andrew Barron
Time: Mon, Wed, Fri 9:25-10:15
Place: DL 220
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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Stochastic Processes (S&DS 351b/551b)
Instructor: Yihong Wu and Sahand Negahban
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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[ Computational Tools for Data Science (S&DS 262b/562b) ]
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Linear Models (S&DS 312a/612a)
Instructor: David Brinda
Time: Mon, Wed 11:35-12:50
Place: WTS A60
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 314a)
Instructor: Winston Lin
Time: Tues, Thurs 4:00-5:15
Place: WLH 211
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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Data Analysis (S&DS 361b/661b)
Instructor: David Brinda
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: Luce 203 (first week)
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: John Lafferty and Derek Feng
Time: Tues, Thurs 9:00-10:15
Place: Loria 250
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: Susan Wang
Time: Mon, Wed 2:30-3:45
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. 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: WLH 117
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 490b) ]
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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)
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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Research Design and Causal Inference (PLSC 508b)
Instructor: Winston Lin
Time: Tues 3:30-5:20
Place: RKZ 08
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[ Applied Linear Models (S&DS 531a) ]
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Introduction to Random Matrix Theory and Applications (S&DS 615b)
Instructor: Zhou Fan
Time: Wed 2:30-5:00
Place: 24 Hillhouse
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Statistical Case Studies (S&DS 625a)
Instructor: Susan Wang
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: Derek Feng
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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Statistical Methods in Computational Biology (S&DS 645b/BIS 692b)
Instructor: Hongyu Zhao
Time: Thur 1:00-2:50
Place: LEPH 115
Introduction to problems, algorithms, and data analysis approaches in computational biology and bioinformatics; stochastic modeling and statistical methods applied to problems such as mapping disease-associated genes, analyzing gene expression microarray data, sequence alignment, and SNP analysis. Statistical methods include maximum likelihood, EM, Bayesian inference, Markov chain Monte Carlo, and some methods of classification and clustering; models include hidden Markov models, Bayesian networks, and the coalescent. The limitations of current models, and the future opportunities for model building, are critically addressed. Prerequisite: STAT 661a, 538a, or 542b. Prior knowledge of biology is not required, but some interest in the subject and a willingness to carry out calculations using R is assumed.
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[ Statistical Computing (S&DS 662b) ]
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Spectral Graph Theory (CPSC 662a)
Instructor: Dan Spielman
Time: Mon, Wed 2:30-3:45
Place: WTS A60
An applied approach to spectral graph theory. The combinatorial meaning of the eigenvalues and eigenvectors of matrices associated with graphs. Applications to optimization, numerical linear algebra, error-correcting codes, computational biology, and the discovery of graph structure.
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Computational Mathematics for Data Science (S&DS 663a)
Instructor: Roy Lederman
Time: Mon, Wed 9:00-10:15
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)
Instructor: Sahand Negahban
Time: Tues 2:30-5:00
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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Topics on Random Graphs (MATH 670)
Instructor: Mathias Schacht
Time: Thurs 8:00-10:00
Place: 17 HH Rm 03
We discuss a variety of topics of the theory of random graphs.

We will introduce the standard models of random graphs and focus on the threshold phenomenon for graph properties. As it turns out for many interesting and natural graph properties the probability for a random graph to enjoy the property moves from close to 0 to close to 1 in a relatively small interval in terms of the given density of the random graph and we investigate this for the properties of (1) containing fixed size or spanning subgraphs (like a perfect matching or a Hamiltonian cycles); (2) for the chromatic number; (3) transferal of classical theorems in extremal combinatorics.

Some background in discrete probability and graph theory is helpful, but the course will be self-contained.
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Selected Topics in Neural Nets (S&DS 671b)
Instructor: Harrison Zhou
Time: Wed 9:00-11:15
Place: 24 Hillhouse
This is a graduate seminar course on some recent theoretical developments in neural nets. The list of topics will include: 1) Nonconvex optimization, 2) Generalization theory, 3) Overparameterization, 4) GAN and VAE, 5) Mean field view, 6) Implicit regularization, 7) Geometry, 8) Statistical theory.
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Information Theory Tools in Probability and Statistics (S&DS 672a)
Instructor: Andrew Barron
Time: Tues 9:00-11:15
Place: 24 Hillhouse
Information theory techniques valuable in probability, statistics, and machine-learning research. Example topics include information inequalities in central limit analysis, in adaptive estimation, in minimax risk determination, in metric entropy calculation, in stochastic search, and in the exploration of the accuracy of deep learning networks. Prerequisite: co-enrollment in S&DS 610, or completion of S&DS 600, or completion of S&DS 664 and S&DS 542.
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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)
Instructor: Roy Lederman
Time: Mon, Wed 11:35-12:50
Place: 24 Hillhouse
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High-Dimensional Statistical Estimation (S&DS 679a)
Instructor: Sahand Negahban
Time: Tues 2:30-5:00
Place: 24 Hillhouse
In this course we will review the recent advances in high-dimensional statistics. We will cover concepts in empirical process theory, concentration of measure, and random matrix theory in the context of understanding the statistical properties of high-dimensional estimation methods. In this discussion we will also overview the computational constraints that are involved with solving high-dimensional problems and touch upon concepts in convex optimization and online learning.
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Statistical Methods in Neuroimaging (S&DS 683a)
Instructor: Dustin Scheinost and Joe Chang
Time: Wed, Fri 2:30-3:45
Place: 17 Hillhouse Rm 115
Introduction to common statistical methods used in neuroimaging. Topics include introduction to different imaging modalities and experimental designs; modeling tasks using linear models; functional connectivity analysis; mixed effects, repeated measures, longitudinal models, power; multiple comparisons, random fields; effective connectivity, dynamic causal modeling, and variational Bayesian methods; machine-learning approaches to multi-voxel pattern analysis.
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Statistical Inference on Graphs (S&DS 684a)
Instructor: Yihong Wu
Time: Wed 2:30-5:00
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)
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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