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.

Contributor(s):

Material type: Text

TextLanguage: English Series: Chapman and Hall/CRC statistics in the social and behavioural sciencesPublication details: Boca Raton : CRC Press, Taylor & Francis Group, 2021Edition: Second editionDescription: xiii, 351 Seiten : Karten, DiagrammeISBN:

9780367568597
9780367341879

Subject(s):

Additional physical formats: Online version:: Big data and social scienceDDC classification:

300.285 23 FOS

Contents:

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

Summary: "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"--

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Holdings
Item type	Current library	Collection	Call number	Status	Barcode
General Books	CUTN Central Library Social Sciences	Non-fiction	300.285 FOS (Browse shelf(Opens below))	Available	50434

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"--

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