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Add to basketGebunden. Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Ian Foster, PhD, is a professor of computer science at the University of Chicago as well as a senior scientist and distinguished fellow at Argonne National Laboratory. His research addresses innovative applications of distributed, parall.
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Published by Chapman and Hall/CRC, 2020
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Buch. Condition: Neu. Big Data and Social Science | Data Science Methods and Tools for Research and Practice | Ian Foster (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2020 | Chapman and Hall/CRC | EAN 9780367341879 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Language: English
Published by Chapman And Hall/CRC, 2020
ISBN 10: 0367341875 ISBN 13: 9780367341879
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Buch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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 not Elektronisches Buch 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.