Big Data and Social Science ─ A Practical Guide to Methods and Tools

The era of Big Data has brought a new analytical paradigm to statisticians and social scientists. This book is the first textbook to provide a practically oriented overview of the analytical and statistical tools associated with big data for social science students. It covers visualization, social media, programming, networks, text analysis, machine learning, privacy, and more. It uses real data examples to illustrate the methods, and to show how this data can be collected, integrated, and analyzed in order to answer complex sociological questions. The book is supported by an online workbook (via Github) that includes computer-based exercises including the data and software for the analyses.

Ian Foster is a professor of computer science at the University of Chicago as well as a senior scientist and distinguished fellow at Argonne National Laboratory. His research addresses innovative applications of distributed, parallel, and data-intensive computing technologies to scientific problems in such domains as climate change and biomedicine. Methods and software developed under his leadership underpin many large national and international cyberinfrastructures. He is a fellow of the American Association for the Advancement of Science, the Association for Computing Machinery, and the British Computer Society. He received a PhD in computer science from Imperial College London.

Rayid Ghani is the director of the Center for Data Science and Public Policy, research director at the Computation Institute, and senior fellow at the Harris School of Public Policy at the University of Chicago. His research focuses on using machine learning and data science for high-impact social good and public policy problems in areas such as education, healthcare, energy, transportation, economic development, and public safety.

Ron S. Jarmin is the assistant director for research and methodology at the U.S. Census Bureau, where he oversees a broad research program in statistics, survey methodology, and economics to improve economic and social measurement within the U.S. federal statistical system. He is the author of many papers in the areas of industrial organization, business dynamics, entrepreneurship, technology and firm performance, urban economics, data access, and statistical disclosure avoidance. He earned a PhD in economics from the University of Oregon.

Frauke Kreuter is a professor at both the University of Maryland and the University of Mannheim. She is also head of the Statistical Methods Group at the Institute for Employment Research in Germany. Among her over 100 publications are several textbooks in survey statistics and data analysis. She established the International Program in Survey and Data Science and is a fellow of the American Statistical Association. She received a PhD from the University of Konstanz.

Julia Lane is a professor at the NYU Wagner Graduate School of Public Service and the NYU Center for Urban Science and Progress. She is also an NYU Provostial Fellow for Innovation Analytics. She co-founded the UMETRICS and STAR METRICS programs at the National Science Foundation, established a data enclave at NORC/University of Chicago, and co-founded the Longitudinal Employer-Household Dynamics Program at the U.S. Census Bureau and the Linked Employer Employee Database at Statistics New Zealand. She is the author/editor of 10 books and the author of over 70 articles in leading journals, including Nature and Science. She is an elected fellow of the American Association for the Advancement of Science and a fellow of the American Statistical Association. She received a PhD in economics from the University of Missouri.