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082			_a300.2856312 _bFOS
245			_aBig data and social science: _bdata science methods and tools for research and practice
250			_a2nd
260			_bCRC Press _aBoco Raton _c2021
300			_axx, 391 p.
365			_aGBP _b48.99
504			_aTable of 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
520			_aBig Data and Social Science: Data Science Methods and Tools for Research and Practice, Second Edition shows how to apply data science to real-world problems, covering all stages of a data-intensive social science or policy project. Prominent leaders in the social sciences, statistics, and computer science as well as the field of data science provide a unique perspective on how to apply modern social science research principles and current analytical and computational tools. The text teaches you how to identify and collect appropriate data, apply data science methods and tools to the data, and recognize and respond to data errors, biases, and limitations.
650			_aSocial sciences--Data processing _910562
650			_aBig data _9212
650			_aData mining _9365
650			_aSocial sciences--Statistical methods _91897
700			_aFoster, Ian _910495
700			_aGhani, Rayid _911348
700			_aJarmin, Ron S. _911349
700			_aKreuter, Frauke _911350
942			_2ddc _cBK