Description
Taylor and Francis Big Data And Social Science Data Science Methods And Tools For Research And Practice 2Nd Edition 2020 Edition by Ian Foster, Rayid Ghani, Ron S. Jarmin
Big 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.Features:Takes an accessible, hands-on approach to handling new types of data in the social sciencesPresents the key data science tools in a non-intimidating way to both social and data scientists while keeping the focus on research questions and purposesIllustrates social science and data science principles through real-world problemsLinks computer science concepts to practical social science researchPromotes good scientific practiceProvides freely available workbooks with data, code, and practical programming exercises, through Binder and GitHubNew to the Second Edition:Increased use of examples from different areas of social sciencesNew chapter on dealing with Bias and Fairness in Machine Learning modelsExpanded chapters focusing on Machine Learning and Text AnalysisRevamped hands-on Jupyter notebooks to reinforce concepts covered in each chapterThis 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. 1. Introduction2. Working with Web Data and APIs - Cameron Neylon3. Record Linkage - Joshua Tokle and Stefan Bender4. Databases - Ian Foster and Pascal Heus5. Scaling up through Parallel and Distributed Computing - Huy Vo and Claudio Silva6. Information Visualization - M. Adil Yalcin and Catherine Plaisant7. Machine Learning - Rayid Ghani and Malte Schierholz8. Text Analysis - Evgeny Klochikhin and Jordan Boyd-Graber9. Networks: The Basics - Jason Owen-Smith10. Data Quality and Inference Errors - Paul P. Biemer11. Bias and Fairness - Kit T. Rodolfa, Pedro Saleiro, and Rayid Ghani12. Privacy and Confidentiality - Stefan Bender, Ron Jarmin, Frauke Kreuter, and Julia Lane13. Workbooks - Brian Kim, Christoph Kern, Jonathan Scott Morgan, Clayton Hunter, and Avishek Kumar