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

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

Chapter 1: Introduction
Chapter I: Capture and Curation
Chapter 2: Working with Web Data and APIs
Chapter 3: Record Linkage
Chapter 4: Databases
Chapter 5: Programming with Big Data

Part II: Modeling and Analysis
Chapter 6: Machine Learning
Chapter 7: Text Analysis
Chapter 8: Networks: The Basics

Part III: Inference and Ethics
Chapter 9: Information Visualization
Chapter 10: Errors and Inference
Chapter 11: Privacy and Confidentiality
Chapter 12: Workbooks