Foster I., Ghani R., Jarmin R.S. et al. Big data and social science: a practical guide to methods and tools

Chapman and Hall/CRC, Taylor & Francis Group, 2017. — 377 p. — (Statistics in the social and behavioral sciences series). — ISBN: 1498751407, 9781498751407Foster I., Ghani R., Jarmin R.S., Kreuter F., Lane J.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.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
Cameron Neylon
Working with Web Data and APIsScraping 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 impactResources
Acknowledgements and copyright
Joshua Tokle and Stefan Bender
Record Linkage
Motivation
Introduction to record linkage
Preprocessing data for record linkage
Indexing and blocking
Matching
Classification
Record linkage and data protectionResources
Ian Foster and Pascal Heus
DatabasesDBMS: When and why
Relational DBMSs
Linking DBMSs and other tools
NoSQL databases
Spatial databases
Which database to use?Resources
Huy Vo and Claudio Silva
Programming with Big DataThe MapReduce programming model
Apache Hadoop MapReduce
Apache SparkResources
Modeling and Analysis
Rayid Ghani and Malte Schierholz
Machine LearningWhat 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 topicsResources
Evgeny Klochikhin and Jordan Boyd-Graber
Text Analysis
Understanding what people write
How to analyze text
Approaches and applications
Evaluation
Text analysis toolsResources
Jason Owen-Smith
Networks: The BasicsNetwork data
Network measures
Comparing collaboration networksResources
Inference and Ethics
M. Adil Yalcın and Catherine Plaisant
Information VisualizationDeveloping effective visualizations
A data-by-tasks taxonomy
ChallengesResources
Paul P. Biemer
Errors and InferenceThe total error paradigm
Illustrations of errors in big data
Errors in big data analytics
Some methods for mitigating, detecting, and compensating for errorsResources
Stefan Bender, Ron Jarmin, Frauke Kreuter, and Julia Lane
Privacy and ConfidentialityWhy is access important?
Providing access
The new challenges
Legal and ethical frameworkResources
Jonathan Scott Morgan, Christina Jones, and Ahmad EmadWorkbooksEnvironmentWorkbook detailsResources