Description
Both Traditional Students and Working ProfessionalsAcquire the Skills to Analyze Social Problems.
Big Data and Social Science: A Practical Guide toMethods and Tools shows how toapply data science to real-world problems in both research and the practice.The book provides practical guidance on combining methods and tools fromcomputer science, statistics, and social science. This concrete approach isillustrated throughout using an important national problem, the quantitativestudy of innovation.
The text draws on the expertise of prominent leadersin statistics, the social sciences, data science, and computer science to teachstudents how to use modern social science research principles as well as thebest analytical and computational tools. It uses a real-world challenge tointroduce how these tools are used to identify and capture appropriate data,apply data science models and tools to that data, and recognize and respond todata errors and limitations.
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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
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