Masters in Computational Social Science is a two year masters program at the University of Chicago that incorporates a fusion of rigorous computer science, computational methods, and social science research curricula. The program culminates with a masters thesis. Preceptor duties include serving as a second reader on student thesis committees, TA'ing core perspectives courses, providing academic advising, and assisting with the Computational Social Science Workshop.
Perspectives on Computational Analysis is a course that surveys computational social science approaches to research. The course reexamines the social scientific method in the context of theoretical development and testing, explores how computation and digital trace data enables new approaches to classic investigations, and considers new ethical challenges. Students review fundamental research designs such as observational studies, experiments, surveys, data visualization, and how computational opportunities can enhance them. The course explores a wide range of contemporary approaches to computational social science, with practical programming assignments to train in these approaches.
Computational Math Camp is a mathematics boot camp to prepare students for graduate social science methods courses. The course seeks to develop skills that social scientists use for the systematic analysis of society, politics, and people. The course provides students with an introduction to the mathematical foundations that form the basis of many methods used in the social sciences. By the end of the course students should have an understanding of basic mathematical operations, a familiarity with core mathematical concepts used in the social sciences, a working knowledge of basic probability theory, and an initial competence with the R programming language.
Machine Learning for Public Policy is an applied course taught in the Harris School of Public Policy. The course is designed to make students better producers and consumers of machine learning and apply these methods to public policy. Students learn to develop machine learning pipelines and refine machine learning models, such as Random Forests or Support Vector Machines in R. Beyond computational skills, students learn to differentiate machine learning methods from statistical and econometric methods typically used in public policy. Students are taught how to think critically about machine learning in public policy, particularly the resource constraints and ethical concerns around how practitioners may implement model results.
Computing for the Social Sciences is an applied Ph.D. and Master's level course taught in the Masters of Computational Social Science program. The course is designed for social scientists with little-to-no programming experience who wish to harness growing digital and computational resources. The focus of the course is on developing a computational framework which students can implement in their own research. Major emphasis is placed on a pragmatic understanding of programming languages and software libraries. Students will leave the course with basic computational skills in a wide variety of techniques and languages, including R and Python. While students will not become expert programmers, they will gain the knowledge of how to adapt and expand these skills as they are presented with new questions and data. Topics include programming fundamentals, data wrangling, data visualization, API queries, web-scraping, topic models, text analysis, machine learning, and web applications.
Social Science Inquiry II is an undergraduate course surveying introductory statistics across a variety of computing platforms including R, Stata, and SPSS. Methods include data manipulation, descriptive graphs, hypothesis testing, two-way tables, linear and multiple regression.
Social Science Inquiry I is an undergraduate course surveying social science research methodology. Methods include survey methods, field experiments, and participant observation. Special emphasis research ethics and the assessment of causal models, counterfactuals, mediator and moderator variables.
Statistical Methods of Research II is a Ph.D. level-course surveying a variety of statistical topics including linear models, logit models, ordered and multinomial logit models, and multiple imputation with chained equations using Stata.
Principal Components and Factor Analysis is a lab module for Advanced Survey Research Methods, a Ph.D. level-course teaching survey questionnaire design, survey sampling methods, survey testing, deployment, and analysis. PCA and EFA taught in lab using Stata to assist students in scale construction and survey evaluation.