Big Data and Social Science: A Practical Guide to Methods and Tools(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.

Table of Contents:
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 DBMSs Linking 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 Analysis Machine Learning Introduction 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 data Network 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