Big Data and Social Science

A Practical Guide to Methods and Tools
1. Auflage 2016
ISBN: 978-1-4987-5140-7
Verlag: Taylor & Francis Inc

Buch, Englisch, 356 Seiten, Format (B × H): 197 mm x 243 mm, Gewicht: 944 g

Reihe: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

Both Traditional Students and Working Professionals Acquire the Skills to Analyze Social Problems.
Big Data and Social Science: A Practical Guide to Methods and Tools shows how to apply data science to real-world problems in both research and the practice. The book provides practical guidance on combining methods and tools from computer science, statistics, and social science. This concrete approach is illustrated throughout using an important national problem, the quantitative study of innovation.
The text draws on the expertise of prominent leaders in statistics, the social sciences, data science, and computer science to teach students how to use modern social science research principles as well as the best analytical and computational tools. It uses a real-world challenge to introduce how these tools are used to identify and capture appropriate data, apply data science models and tools to that data, and recognize and respond to data errors and limitations.
For more information, including sample chapters and news, please visit the author's website.

Autoren/Hrsg.

Foster, Ian

Ghani, Rayid

Jarmin, Ron S.

Kreuter, Frauke

Lane, Julia

Inhaltsverzeichnis

Introduction Why this book? Defining big data and its value Social science, inference, and big data Social science, data quality, and big data New tools for new data The book’s "use case" The structure of the book Resources

Capture and Curation Working with Web Data and APIs Introduction Scraping information from the web New data in the research enterprise A functional view Programming against an API Using the ORCID API via a wrapper Quality, scope, and management Integrating data from multiple sources Working with the graph of relationships Bringing it together: Tracking pathways to impact Summary Resources Acknowledgements and copyright

Record Linkage Motivation Introduction to record linkage Preprocessing data Classification Record linkage and data protection Summary Resources

Databases Introduction DBMS: When and why Relational DBMSsLinking DBMSs and other tools NoSQL databases Spatial databases Which database to use? Summary Resources

Programming with Big Data Introduction The MapReduce programming model Apache Hadoop MapReduce Apache Spark Summary Resources

Modeling and AnalysisMachine LearningIntroduction What is machine learning? The machine learning process Problem formulation: Mapping a problem to machine learning methods Methods Evaluation Practical tips How can social scientists benefit from machine learning? Advanced topics Summary Resources

Text Analysis Understanding what people write How to analyze text Approaches and applications Evaluation Text analysis tools Summary Resources

Networks: The Basics Introduction Network dataNetwork measures Comparing collaboration networks Summary Resources

Inference and Ethics Information Visualization Introduction Developing effective visualizations A data-by-tasks taxonomy Challenges Summary Resources

Errors and Inference Introduction The total error paradigm Illustrations of errors in big data Errors in big data analytics Some methods for mitigating, detecting, and compensating for errors Summary Resources

Privacy and Confidentiality Introduction Why is access at all important? Providing access The new challenges Legal and ethical framework Summary Resources

Workbooks Introduction Environment Workbook details Resources
Bibliography

Über Autor(innen)

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.