21 August 2018 Course List Fall 2018/Spring 2019 FALL 10:00 AM, Tuesday, August 28, 2018 Dan Spielman (Spring) and Sekhar Tatikonda (Fall) David Pollard and Andrew Barron David Pollard ALL id, semester, number, name, shortname, level, instructor, time, classroom, webpage, description, extra, extratime, extraroom &100 SPRING S&DS 100b/500b x Introductory Statistics Intro Stat intro, no prerequisites David Brinda Mon, Wed, Fri 10:30-11:20 TBA 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. 100.5 FALL S&DS 101a-106a/501a-506a x Intro Stat intro, no prerequisites Jonathan Reuning-Scherer and Staff Tues, Thurs 1:00-2:15 OML 202 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. //PAR 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. -101 FALL S&DS 101a/501a E&EB 210aG/MCDB 215a Introduction to Statistics: Life Sciences intro, no prerequisites Jonathan Reuning-Scherer and Walter Jetz Tues, Thurs 1:00-2:15 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. -102 FALL S&DS 102a/502a EP&E 203a/PLSC 425a Introduction to Statistics: Political Science intro, no prerequisites Jonathan Reuning-Scherer and Kelly Rader Tues, Thurs 1:00-2:15 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. -103 FALL S&DS 103a/503a SOCY 119a Introduction to Statistics: Social Sciences Jonathan Reuning-Scherer Tues, Thurs 1:00-2:15 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. -104 S&DS 104a/504a PSYC 201a Introduction to Statistics: Psychology XXX not taught this year 123 SPRING S&DS 123b YaleData YaleData Jessi Cisewski Mon, Wed, Fri 10:30-11:20 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. 124 XXX S&DS 124b - YData: Lab Course YData: Lab Course Jessi Cisewski TBD TBD Needed. 150 SPRING S&DS 150b 150 Data Science Ethics Data Science Ethics Elisa Celis Tues, Thurs 1:00-2:15 TBD Needed.