Big data and social science : data science methods and tools for research and practice /
Big data and social science : data science methods and tools for research and practice /
edited by Ian Foster, Rayid Ghani, Ron S. Jarmin, Frauke Kreuter and Julia Lane.
- Second edition.
- Boca Raton : CRC Press, Taylor & Francis Group, 2021
- xiii, 351 Seiten : Karten, Diagramme
- Chapman and Hall/CRC statistics in the social and behavioural sciences .
Includes bibliographical references and index.
1. Introduction
2. Working with Web Data and APIs - Cameron Neylon
3. Record Linkage - Joshua Tokle and Stefan Bender
4. Databases - Ian Foster and Pascal Heus
5. Scaling up through Parallel and Distributed Computing - Huy Vo and Claudio Silva
6. Information Visualization - M. Adil Yalcin and Catherine Plaisant
7. Machine Learning - Rayid Ghani and Malte Schierholz
8. Text Analysis - Evgeny Klochikhin and Jordan Boyd-Graber
9. Networks: The Basics - Jason Owen-Smith
10. Data Quality and Inference Errors - Paul P. Biemer
11. Bias and Fairness - Kit T. Rodolfa, Pedro Saleiro, and Rayid Ghani
12. Privacy and Confidentiality - Stefan Bender, Ron Jarmin, Frauke Kreuter, and Julia Lane
13. Workbooks - Brian Kim, Christoph Kern, Jonathan Scott Morgan, Clayton Hunter, and Avishek Kumar
"This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner"--
9780367568597 9780367341879
Social sciences
Social sciences
Data mining.
Big data.--Data processing.--Statistical methods.
300.285 / FOS
Includes bibliographical references and index.
1. Introduction
2. Working with Web Data and APIs - Cameron Neylon
3. Record Linkage - Joshua Tokle and Stefan Bender
4. Databases - Ian Foster and Pascal Heus
5. Scaling up through Parallel and Distributed Computing - Huy Vo and Claudio Silva
6. Information Visualization - M. Adil Yalcin and Catherine Plaisant
7. Machine Learning - Rayid Ghani and Malte Schierholz
8. Text Analysis - Evgeny Klochikhin and Jordan Boyd-Graber
9. Networks: The Basics - Jason Owen-Smith
10. Data Quality and Inference Errors - Paul P. Biemer
11. Bias and Fairness - Kit T. Rodolfa, Pedro Saleiro, and Rayid Ghani
12. Privacy and Confidentiality - Stefan Bender, Ron Jarmin, Frauke Kreuter, and Julia Lane
13. Workbooks - Brian Kim, Christoph Kern, Jonathan Scott Morgan, Clayton Hunter, and Avishek Kumar
"This classroom-tested book fills a major gap in graduate- and professional-level data science and social science education. It can be used to train a new generation of social data scientists to tackle real-world problems and improve the skills and competencies of applied social scientists and public policy practitioners. It empowers you to use the massive and rapidly growing amounts of available data to interpret economic and social activities in a scientific and rigorous manner"--
9780367568597 9780367341879
Social sciences
Social sciences
Data mining.
Big data.--Data processing.--Statistical methods.
300.285 / FOS