Big Data And Social Science (Statistics In The Behavioral Sciences Series) Ian Foster, Rayid Ghani, Ron S. Jarmin
(Statistics%20in%20the%20social%20and%20behavioral%20sciences%20series)%20Ian%20Foster%2C%20Rayid%20Ghani%2C%20Ron%20S.%20Jarmin
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Page Count: 377 [warning: Documents this large are best viewed by clicking the View PDF Link!]
- Cover
- Half Title
- Title
- Copyright
- Contents
- Preface
- Editors
- Contributors
- 1: Introduction
- I: Capture and Curation
- 2: Working with Web Data and APIs
- 2.1: Introduction
- 2.2: Scraping information from the web
- 2.3: New data in the research enterprise
- 2.4: A functional view
- 2.5: Programming against an API
- 2.6: Using the ORCID API via a wrapper
- 2.7: Quality, scope, and management
- 2.8: Integrating data from multiple sources
- 2.9: Working with the graph of relationships
- 2.10: Bringing it together: Tracking pathways to impact
- 2.11: Summary
- 2.12: Resources
- 2.13: Acknowledgements and copyright
- 3: Record Linkage
- 4: Databases
- 5: Programming with Big Data
- 2: Working with Web Data and APIs
- II: Modeling and Analysis
- 6: Machine Learning
- 6.1: Introduction
- 6.2: What is machine learning?
- 6.3: The machine learning process
- 6.4: Problem formulation: Mapping a problem to machine learning methods
- 6.5: Methods
- 6.6: Evaluation
- 6.7: Practical tips
- 6.8: How can social scientists benefit from machine learning?
- 6.9: Advanced topics
- 6.10: Summary
- 6.11: Resources
- 7: Text Analysis
- 8: Networks: The Basics
- 6: Machine Learning
- III: Inference and Ethics
- 9: Information Visualization
- 10: Errors and Inference
- 10.1: Introduction
- 10.2: The total error paradigm
- 10.3: Illustrations of errors in big data
- 10.4: Errors in big data analytics
- 10.5: Some methods for mitigating, detecting, and compensating for errors
- 10.6: Summary
- 10.7: Resources
- 11: Privacy and Confidentiality
- 12: Workbooks
- Bibliography
- Index