The course treats methods together with mathematical frameworks that provide intuition and justifications for how and when the methods work. Problems are drawn from genetics, ecology, epidemiology, and bioinformatics. Elisa Celis, an assistant professor of statistics and data science at FAS, who analyzes the objectivity of data used in a variety of algorithms affecting everything from politics and policing to consumer behavior. S&DS123 The M.A. While the main purpose of some of these courses is not computing, students who have taken at least two of these courses will be capable of digesting and processing data. Practical statistical analysis also uses a variety of computational techniques, methods of visualizing and exploring data, methods of seeking and establishing structure and trends in data, and a mode of questioning and reasoning that quantifies uncertainty. Advanced text-mining analysis in the R package tm and web-scraping algorithms in Python are applied to both archival and current data to enhance analysis. Econ 136 may be substituted for S&DS 242. Faculty and students are also active in collaborative research with other departments throughout the university, including astrophysics, computer science, genetics, economics, radiology, engineering, bioinformatics, economics. Problems presented with reference to a wide array of examples: public opinion, campaign finance, racially motivated crime, and public policy. in Public Health, or an M.A. Contact covers essential ideas of probability and statistics, together with an introduction to data analysis using modern computational tools. under which the original cause of slow convergence will persist. QRTTh 9am-10:15am, S&DS230a or b, Data Exploration and Analysis Staff, Survey of statistical methods: plots, transformations, regression, analysis of variance, clustering, principal components, contingency tables, and time series analysis. 121 programs offered by Yale University. Helpful Tips on using the Interactive Tool: There is no Enter or Submit Button - Results will appear automatically with your selections . 1 probability and statistical theory course; 2 statistical methodology and data analysis courses; 1 computational and machine learning course; and 2 half-credit courses or 1 course in discipline area, as specified, Programs and Certificates in Yale College. Each course in the S&DS 101106 group emphasizes 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 (life sciences, political science, social sciences, medicine, or data analysis). This can be either of: Two of the 12-credit seminars (S&DS 170, 171 and 172) that accompanied S&DS 123 in Spring 2019. The Yale Statistical Machine Learning Group carries out research and training in machine learning with an emphasis on statistical analysis and principles. Advanced students may substitute S&DS 351 or 364 or EENG 431. QRTTh 1pm-2:15pm, S&DS364b / AMTH364b / EENG454b, Information Theory Andrew Barron, Foundations of information theory in communications, statistical inference, statistical mechanics, probability, and algorithmic complexity. Currently in California, he reads textbooks for classes he plans to take once he's back in New Haven. YData is an introduction to Data Science that emphasizes the development of these skills while providing opportunities for hands-on experience and practice. . We are interested in a broad range of topics in . I am an Assistant Professor at the Department of Statistics and Data Science at Yale University. QRTTh 1pm-2:15pm, S&DS108a, Introduction to Statistics: Advanced Fundamentals Jonathan Reuning-Scherer, Introductory statistical concepts beyond those covered in high school AP statistics. Examples of such courses include: CPSC223, 323, 424, 437. Prerequisites: a background in probability and statistics at the level of S&DS242; familiarity with the core ideas from linear algebra, for example through Math 222; and computational skills at the level of S&DS265 or CPSC200. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. offers the same introduction to statistics as the 101106 group, but without applications to a specific field. QRTTh 9am-10:15am, S&DS101a / E&EB210a, Introduction to Statistics: Life Sciences Walter Jetz and Jonathan Reuning-Scherer, Statistical and probabilistic analysis of biological problems, presented with a unified foundation in basic statistical theory. Multivariable calculus is required and should be taken before or during the sophomore year. S&DS Seminar: Lu Lu (University of Pennsylvania) On February 27, 2023 at 4:00 pm. The PDF will include all information unique to this page. Workshop Calendar Essential Resources Computational and Inferential Thinking: The Foundations of Data Science See Academic Regulations, section L, Special Academic Arrangements, "Simultaneous Award of the Bachelor's and Master's Degrees." Probability and Statistical Theory: one of S&DS 238, 240, 241, 242. Students must complete a research project to be eligible for Distinction in the Major. The Data Science in a Discipline Area courses for the data science. Students gain the necessary knowledge base and useful skills to tackle real-world data analysis challenges. ), As a projection-free algorithm, Frank-Wolfe (FW) method, also known as conditional gradient, has recently received considerable . We read critical commentary by practitioners, state-of-the-art technical papers by data scientist and computer scientists, and samples of legal scholarship, moral and ethical philosophy, readings in sociology, and policy documents. CPSC 323 may be substituted for CPSC 223. In this dissertation, we study several topics on the FW variants for scalable The mathematical foundation of statistics lies in the theory of probability, which is applied to problems of making inferences and decisions under uncertainty. This tool allows users to search outcomes by year and major. (203) 432-1775, Beinecke Rare Book and Manuscript Library, Accessibility Diversity, Equity, and Inclusion Giving Privacy and Data Use Contact Our Web Team, 2022 Yale University Library All Rights Reserved. ), One of the Data Science in a Discipline Area courses approved for the data science, ANTH 376 (Observing and Measuring Behavior), ASTR 255 (Research Methods in Astrophysics), ASTR 330 (Scientific Computing in Astrophysics), ASTR 356 (Astrostatistics and Data Mining), BENG 469 (Single-cell Biologies, Technologies, and Analysis), ECON 438 (Applied Econometrics: Politics, Sports, Microeconomics), GLBL 191 (Research Design and Survey Analysis), MB&B 452 / MCDB 452 / S&DS 352 (Biomedical Data Science, Mining and Modeling), PLSC 340 / S&DS 315 (Measuring Impact and Opinion Change), PLSC 341 / GLBL 195 (Logic of Randomized Experiments in Political Science), PLSC 438 (Applied Quantitative Research Design), PLSC 454 (Data Science for Politics and Policy), PSYC 235 (Research Methods in Psychology), PSYC 238 (Research Methods in Decision Making and Happiness), PSYC 258 / NSCI 258 (Computational Methods in Human Neuroscience), PSYC 438 / NSCI 441 (Computational Models of Human Behavior), S&DS 171 (YData: Text Data Science: An Introduction) if taken in Spring 2020 or later, S&DS 172 (YData: Data Science for Political Campaigns)if taken in Spring 2020 or later, S&DS 173 (YData: Analysis of Baseball Data) if taken in Spring 2020 or later, S&DS 174 (YData: Statistics in the Media), S&DS 177 (YData: Covid-19 Behavorial Impacts). and the 101106 group provide an introduction to statistics and data science with no mathematics prerequisite. This first panel, featuring Gabriel Acevedo (Research Analyst & Statistician at Institutional Research and PhD '05, Sociology) and Kayla Schipp (Program Manager at Yale Digital Humanities Lab and PhD, Emory, English) focuses on careers in universities that employ research skills. Selection of materials focuses on English-language titles. in Music, be sure to use the Graduate School of Arts and Sciences Ph.D./Master's . QRTTh 1pm-2:15pm, S&DS103a / EP&E209a / PLSC453a, Introduction to Statistics: Social Sciences Jonathan Reuning-Scherer, Descriptive and inferential statistics applied to analysis of data from the social sciences. In the first chapter, a subsequence-based variational Bayesian inference QRHTBA, S&DS431a / AMTH431a, Optimization and Computation Yang Zhuoran, This course is designed for students in Statistics & Data Science who need to know about optimization and the essentials of numerical algorithm design and analysis. Designed for students who've completed the MITx MicroMasters in Statistics and Data Science, this degree pathway offers a core curriculum of data science. QRTTh 11:35am-12:50pm, S&DS365a, Intermediate Machine Learning John Lafferty, S&DS365 is a second course in machine learning at the advanced undergraduate or beginning graduate level. In this course, we explore how data science is being used to design winning campaigns. Check out tuition fees, course rankings, entry requirements, application deadlines, and course reviews. requirements. About. S&DS 430a/630a ENAS 530a EENG 437a ECON 413a, http://www.stat.yale.edu/Courses/QR/stat101106.html, http://www.stat.yale.edu/Seminars/2011-12/. The group is directed by Prof. John Lafferty in the Department of Statistics and Data Science within the Faculty of Arts and Sciences at Yale. Students intending to major in Statistics and Data Science should consult the department guide and FAQ. Tuition | Yale Graduate School of Arts & Sciences Tuition Tuition for full-time study at the Graduate School of Arts and Sciences for the academic year 2022-2023 is $46,900. Students should consider S&DS 103 or both S&DS 108, 109. flattens the posterior by reducing the size of the observed subsample. Topics covered include convex analysis; duality and KKT conditions; subgradient methods; interior point methods; semidefinite programming; distributed methods; stochastic gradient methods; robust optimization; and an introduction to nonconvex optimization. Right now,. attention in the machine learning community. Other courses for nonmajors include S&DS110 and 160. Many academic programs, such as Economics, Management, Political Science, Psychology, and Sociology use statistical methodologies in their teaching and research and are supported by the collections. W.L. Programming is central to the course, and is based on the Python programming language. The incumbent, as an expert in applied statistics, will contribute to the integrated research and . The simulated tempering algorithm uses an auxiliary Prerequisites: Two of the following courses: S&DS230, 238, 240, 241 and 242; previous programming experience (e.g., R, Matlab, Python, C++), Python preferred. BOX 208240 QRW 1:30pm-3:20pm, S&DS220b, Introductory Statistics, Intensive Robert Wooster, Introduction to statistical reasoning for students with particular interest in data scienceand computing. Foreign Assistance to Sub-Saharan Africa: Archival Data Analysis, YData: Data Science for Political Campaigns, Numerical Linear Algebra: Deterministic and randomized algorithms, Computational Mathematics for Data Science, Intensive Introductory Statistics and Data Science, Biomedical Data Science, Mining and Modeling, Multivariate Statistics for Social Sciences, Applied Machine Learning and Causal Inference Research Seminar, High-dimensional phenomena in statistics and learning, Statistics and Data Science Computing Laboratory (1/2 credit), YData: Text Data Science: An Introduction, Applied Machine Learning and Causal Inference, Selected Topics in Statistical Decision Theory, Introduction to Random Matrix Theory and Applications, Probabilistic Networks, Algorithms, and Applications, Nonparametric Estimation and Machine Learning, Information Theory Tools in Probability and Statistics, High-Dimensional Function Estimation (prev title). QRMW 1pm-2:15pm, S&DS352b / MB&B452b / MCDB452b, Biomedical Data Science, Mining and Modeling Mark Gerstein, Techniques in data mining and simulation applied to bioinformatics, the computational analysis of gene sequences, macromolecular structures, and functional genomics data on a large scale. 4 Statistics and Data Science can be taken either as a primary major or as one of two majors, in consultation with the DUS. In this guide, you will discover resources at Yale that support statistics, data science and related fields. SOTTh 2:30pm-3:45pm, * S&DS150a, Data Science Ethics Elisa Celis, In this course, we introduce, discuss, and analyze ethical issues, algorithmic challenges, and policy decisions that arise when addressing real-world problems via the lens of data science. Application of statistical concepts to data; analysis of real-world problems. Ph.D Biological Sciences (1) Ph.D Computer Science (1) Ph.D Data . Copyright 2022 Yale University All rights reserved Contact Us. 121 programs offered by Yale University. communication-efficient distributed FW framework for both convex and non-convex objective functions. Computer science is one of the most common subjects that online learners study, and data science is no exception. 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&DS123, YData. P.O. 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. QRMW 11:35am-12:50pm, S&DS351b / EENG434b / MATH251b, Stochastic Processes Amin Karbasi, Introduction to the study of random processes including linear prediction and Kalman filtering, Poison counting process and renewal processes, Markov chains, branching processes, birth-death processes, Markov random fields, martingales, and random walks. The Department of Statistics and Data Science has active research programs in statistical information theory, statistical genetics and bioinformatics, Bayesian methods, statistical computing, graphical methods, model selection, and asymptotics. Meets for the second half of the term only. Prerequisites: One from S&DS238, S&DS241, S&DS242, or the equivalent; and one from S&DS230, ECON131, or the equivalent. The Engineering & Applied Science Librarian focuses on titles in mathematical and theoretical statistics. Students who wish to major in Statistics and Data Science are encouraged to take S&DS220 or a 100-level course followed by S&DS230. S&DS Assistant Professor Roy Lederman receives 2023 Sloan Research Fellowship, an award that recognizes outstanding early-career Daniel Spielman, Sterling Professor of Computer Science, Statistics and Data Science, and Mathematics, is the inaugural James A. Attwood Director of the new institute. Statistics and Data Science: Welcome Welcome Tutorials for STATA & R Tables and Formulas Managing Your Research Welcome! New Haven, CT Statistics and Data Science (S&DS) S&DS 100b, Introductory Statistics Ethan Meyers An introduction to statistical reasoning. degree. . Mathematical graphs provide a simple common language to describe the variety of networks and their properties. They should not be introductory statistics or probability courses within that discipline, nor should they be courses that focus on statistical methods for analyzing data that has already been cleaned. Computation & Machine LearningOne from S&DS262, 265,317, 365, CPSC223, 477, PHYS378, PLSC468. A note to students applying to one of Yale's professional schools or programs: If you are applying for a Ph.D. in Architecture, Environment, Investigative Medicine, Law, Management, Music, Nursing, or Public Health, or for an M.S. Prerequisites Both degreesone of MATH120, ENAS151, MATH230, MATH302, or equivalent, Number of courses B.A.11 term courses beyond prereqs (incl senior req); B.S.14 term courses beyond prereqs (incl senior req), Specific courses required B.A.MATH222 or 225or MATH226; B.S.same, plus 1 Core Probability and Statistics course must be S&DS242; and for the Class of 2024 and beyond, 1 Methods of Data Science course must be S&DS365, Distribution of courses B.A.2 courses from Core Probability and Statistics, 2 courses from Computational Skills, 2 courses from Methods of Data Science, and 3 electives chosen from any discipline area with DUS approval; B.S.same, plus 1 Mathematical Foundations and Theory course and 2 additional electives from any discipline area (except Data Science in Context and Methods in Application Areas) with DUS approval, Senior requirement Both degreesSenior Project (S&DS491 or S&DS492) or Statistical Case Studies (S&DS425). prepares students to participate in research efforts or to pursue graduate school in the study of data science. and M.S. This course is not open to students who have taken S&DS430. Privacy policy For more information, please see: Research Opportunities in Data Science and Fundamental Physics at Wright Lab. We often ground our discussions around recent case studies, controversies, and current events. May not be taken after S&DS101106 or 109. Data Science in Context Students are encouraged to take courses that involve the study of data in application areas. Linear Algebra, by MATH222, 223 or 230 or 231;Graph Theory, by MATH244 or CPSC365 or 366; and comfort with proof-based exposition and problem sets, such as is gained from MATH230 and 231, or CPSC366.TTh 1pm-2:15pm, S&DS432b, Advanced Optimization Techniques Sekhar Tatikonda, This course covers fundamental theory and algorithms in optimization, emphasizing convex optimization. Privacy policy Statistics and data science is the art of answering complex questions from numerical facts, called data. Yale University Careers New Haven, CT. Prerequisites: S&DS 541 and S&DS 542 or equivalent, or permission of the instructor. The Certificate in Data Science is designed for students majoring in disciplines other than Statistics & Data Science to acquire the knowledge to promote mature use of data analysis throughout society. Students gain the necessary knowledge base and useful skills to tackle real-world data analysis challenges. The collections primary function is to support research and teaching programs concerned with data science and its application in different fields; statistics as related to applied mathematics, not as a form of numeric information. two leading to an M.A. measurement error models are explored in three chapters. to update the optimization variable and yet achieves the best known complexity bounds for convex, non-convex, and monotone Data science is used to inform where campaigns compete, which messages they use, how they deliver them, and among which voters. Worldwide with concentrations in United States of America. Examples of such courses include: ANTH376, EVST362, GLBL191, 195, LING229, 234, 380, PLSC454, PSYC258. Mathematical Foundations and Theory All students in the major must know linear algebra as taught in MATH222 or 225or 226. In addition, there are associated YData seminars, half-credit courses in a specific domain developed for extra hands-on experience motivated by real problems in a specific domain. Essentials of probability, including conditional probability, random variables, distributions, law of large numbers, central limit theorem, and Markov chains. No course may be applied to satisfy the requirements of both a major and the certificate. temperature variable to flatten the target density (reducing the effective cluster separation). MD, MHS, Department of . Check Detailed . Introduction to regression and linear models. Data Science and Analytics Computer Science and Engineering Business Health Care Design Engineering Statistics Mathematics Law Architecture View All. They should be courses that teach students about the use of data within the domain, including issues of data collection and handling messy data. QRTTh 1pm-2:15pm, S&DS240a, An Introduction to Probability Theory Robert Wooster, Introduction to probability theory. Toggle navigation. My research interests lie at the intersection of mathematical statistics, probability theory, computational algorithms, and applications in genetics and computational biology. The Ph.D. program in Statistics and Data Science The terminal M.A. The same form can also be used to un-register. Topics include numerical and graphical summaries of data, data acquisition and experimental design, probability, hypothesis testing, confidence intervals, correlation and regression. Department of Statistics and Data Science News Job Openings Department of Statistics and Data Science is conducting an open field / open rank search. New Haven, CT 06511. These course selections should be approved by the director of undergraduate studies (DUS). Employment: Assistant Professor Jan 2019-Present Department of Statistics and Data Science Yale University Senior Research Scientist June 2014-Dec 2018 School of Computer and Communication Sciences (IC) degree in Statistics and Data Science. The new undergraduate major in Statistics and Data Science was approved by the Yale College Faculty on March 2nd! Bachelor of Science [B.S] Statistics and Data Science. primarily for graduate students. Combined B.S./M.A. The major is sponsored by the newly renamed Department of Statistics and Data Science. Yale will officially offer a data science and statistics major, after a Thursday vote at the Yale College faculty meeting set the University on track to become one of the first institutions in the country to host a full-scale department with "data science" in its title. Topics include linear and nonlinear models, maximum likelihood, resampling methods, curve estimation, model selection, classification, and clustering. Full Time. (S&DS 171 and 172 are now offered as full-credit courses, so either course can be used on its own to satisfy this requirement if taken in Spring 2020 or later. Topics include nonparametric regression and classification, kernel methods, risk bounds, nonparametric Bayesian approaches, graphical models, attention and language models, generative models, sparsity and manifolds, and reinforcement learning. In this thesis, The computing materials will be hosted on a special purpose web server. INR 57 L/Yr USD 68,831 /Yr. Topics include probability spaces, random variables, expectations and probabilities, conditional probability, independence, discrete and continuous distributions, central limit theorem, Markov chains, and probabilistic modeling. Statistical Methodology and Data Analysis: two of S&DS 230, 242, 312, 361, 363, PLSC 349. Students require background in linear algebra, multivariable calculus, probability and programming. For students with sufficient preparation in mathematics, S&DS238 I am also a member of the Quantitative Biology Institute (QBio) and the Wu Tsai Institute (WTI) at Yale. The major is sponsored by the newly renamed Department of Statistics and Data Science. Organizational Meeting for all 2 Statistics and Data Science (S&DS) S&DS 109a, Introduction to Statistics: Fundamentals Jonathan Reuning-Scherer General concepts and methods in statistics. 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