Big Data and Social Science

Big Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations.Features:Takes an accessible, hands-on approach to handling new types of data in the social sciencesPresents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposesIllustrates social science and data science principles through real-world problemsLinks computer science concepts to practical social science researchPromotes good scientific practiceProvides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHubNew to the Second Edition:Increased use of examples from different areas of social sciencesNew chapter on dealing with Bias and Fairness in Machine Learning modelsExpanded chapters focusing on Machine Learning and Text AnalysisRevamped hands-on Jupyter notebooks to reinforce concepts covered in each chapterThis classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner.

"Like the first edition, the new edition will continue to play an important role for the intended audience and a wider professional community. The much-needed second edition is timely and showcases a wide range of examples and application examples from different areas of the social sciences to demonstrate how the methods are implemented using several real datasets. As expected with this kind of book, the topics of this text are diverse in nature, but interesting none the less. As it is well known, machine learning techniques are subject to inherited bias in model selection and consequently negatively impacts post estimation and prediction. This new edition includes a new chapter on dealing with bias and fairness in machine learning models, a much-needed fair and welcome edition! Further, the authors have done an excellent job in expanding the material on machine learning and text analysis. Like the first edition, the main strength of the book is that it offers a wide variety of applications that are based on real datasets emerging from social science perspectives and useful for both academic and professional communes. As Jupyter has become more popular as the data scientists’ computational notebook of choice, the book has new and improved hands-on Jupyter notebooks to complement each chapter’s material. In conclusion, this new edition has an impressive collection of material on useful and interesting topics on big data. The book will be equally useful to graduate students and researchers interested in gaining perspectives and knowledge on this important topic. The new volume comprises of a wealth of information, a kind of one-stop shop, and can be served as a textbook and research reference book." - S. Ejaz Ahmed, Brock University, CanadaPraise For First Edition"This book builds a nice bridge connecting social science and big data methodology. Big data such as social media and electronic health records, empowered by the advances in information technology, are an emerging phenomenon in recent years and present unprecedented opportunities for social science research. This book was written by pioneering scientists in applying big data methods to address social science problems. As shown by numerous examples in the book, social science could benefit significantly by embracing the new mode of big data and taking advantage of the technical progress in analysing such data. If you work in social science and would like to explore the power of big data, this book is clearly for you. Indeed, if you do not have previous experience in dealing with big data, you should read this book first, before implementing a big-data project. As indicated by the title, this book acts as a practical guide and targets readers with minimum big data experience, hence it is very hands-on. … It covers all necessary steps to finish a big data project: collecting, "The book includes a large volume of condensed information in which the concepts are very well sketched around the right questions along with examples explained in detail. [...] Through the approached topics, the way of structuring and presenting information, the wealth of resources provided “Big Data and Social Science: A Practical Guide to Methods and Tools” offers to social sciences students and researches a high-quality theoretical and practical content."-Anca Vitcu, International Society for Clinical Biostatistics, 72, 2021Praise For First Edition"This book builds a nice bridge connecting social science and big data methodology. Big data such as social media and electronic health records, empowered by the advances in information technology, are an emerging phenomenon in recent years and present unprecedented opportunities for social science research. This book was written by pioneering scientists in applying big data methods to address social science problems. As shown by numerous examples in the book, social science could benefit significantly by embracing the new mode of big data and taking advantage of the technical progress in analysing such data. If you work in social science and would like to explore the power of big data, this book is clearly for you. Indeed, if you do not have previous experience in dealing with big data, you should read this book first, before implementing a big-data project. As indicated by the title, this book acts as a practical guide and targets readers with minimum big data experience, hence it is very hands-on. … It covers all necessary steps to finish a big data project: collecting raw data, cleaning and preprocessing data, applying various modelling tools to analyze the data, evaluating results, protecting privacy, and addressing ethical problems. … All the important topics concerning big data are covered, making this book a good reference that you should always keep on your desk." — Guoqiang Yu, Virginia Tech, in Journal of the American Statistical Association, July 2017"…In summary, although there is a growing number of books related to social science and big data, this volume contains several non-trivial aspects which make it worth to have in the library, possibly along with other similar textbooks as a good complement to them." —Stefano M. Iacus, University of Milan, in Journal of Statistical Software, June 2017"This is a well-written book and showcases a good number of examples and applications to demonstrate how the methods are actually used in real life situation using real datasets. Further, topics at hand are motivated by social science data. … The chapters are nicely structured, well presented and motivated by data examples. The main strength of the book is that it still offers a good number of applications that are based on real datasets emerging from social science perspectives.

Ian Foster, PhD, 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 earned a PhD in computer science from Imperial College London.Rayid Ghani is a professor in the Machine Learning Department (in the School of Computer Science) and the Heinz College of Information Systems and Public Policy at Carnegie Mellon University. His research focuses on developing and using Machine Learning, AI, and Data Science methods for solving high impact social good and public policy problems in a fair and equitable way across criminal justice, education, healthcare, energy, transportation, economic development, workforce development and public safety. He is also the founder and director of the “Data Science for Social Good” summer program for aspiring data scientists to work on data mining, machine learning, big data, and data science projects with social impact. Previously Rayid Ghani was a faculty member at University of Chicago, and prior to that, served as the Chief Scientist for Obama for America (Obama 2012 Campaign).Ron Jarmin, PhD, is the Deputy Director at the U.S. Census Bureau. He earned a PhD in economics from the University of Oregon and has published in the areas of industrial organization, business dynamics, entrepreneurship, technology and firm performance, urban economics, Big Data, data access and statistical disclosure avoidance. He oversees the Census Bureau’s large portfolio of data collection, research and dissemination activities for critical economic and social statistics including the 2020 Decennial Census of Population and Housing.Frauke Kreuter, PhD, is Professor at the University of Maryland in the Joint Program in Survey Methodology, Professor of Statistics and Methodology at the University of Mannheim and head of the Statistical Methods group at the Institute for Employment Research in Nuremberg, Germany. She is founder of the International Program in Survey and Data Science, co-founder of the Coleridge Initiative, fellow of the American Statistical Association (ASA), and recipient of the WSS Cox and the ASA Links Lecture Awards. Her research focuses on data quality, privacy, and the effects of bias in data collection on statistical estimates and algorithmic fairness.Julia Lane, PhD, is a professor at the NYU Wagner Graduate School of Public Service. She is also an NYU Provostial Fellow for Innovation Analytics. She co-founded the Coleridge Initiative as well as UMETRICS and STAR METR