Data Science for Public Policy (Springer Series in the Data Sciences) - Softcover

Buch 7 von 11: Springer Series in the Data Sciences

Chen, Jeffrey C.; Rubin, Edward A.; Cornwall, Gary J.

 
9783030713546: Data Science for Public Policy (Springer Series in the Data Sciences)
Softcover
ISBN 10: 3030713547 ISBN 13: 9783030713546
Verlag: Springer, 2022

Inhaltsangabe

This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data.

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Über die Autorin bzw. den Autor

Jeffrey C. Chen: (1) Affiliated Researcher, Bennett Institute for Public Policy, University of Cambridge
Edward A. Rubin: (1) Assistant Professor, University of Oregon (Dept. of Economics)
Gary J. Cornwall: (1) Research Economist, U.S. Bureau of Economic Analysis

    Von der hinteren Coverseite

    This textbook presents the essential tools and core concepts of data science to public officials, policy analysts, and economists among others in order to further their application in the public sector. An expansion of the quantitative economics frameworks presented in policy and business schools, this book emphasizes the process of asking relevant questions to inform public policy. Its techniques and approaches emphasize data-driven practices, beginning with the basic programming paradigms that occupy the majority of an analyst’s time and advancing to the practical applications of statistical learning and machine learning. The text considers two divergent, competing perspectives to support its applications, incorporating techniques from both causal inference and prediction. Additionally, the book includes open-sourced data as well as live code, written in R and presented in notebook form, which readers can use and modify to practice working with data.

    „Über diesen Titel“ kann sich auf eine andere Ausgabe dieses Titels beziehen.

    Verlag
    Springer
    Erscheinungsdatum
    2022
    Sprache
    Englisch
    ISBN 10 
    3030713547
    ISBN 13 
    9783030713546
    Einband
    Tapa blanda
    Auflage
    1
    Anzahl der Seiten
    380
    Kontakt zum Hersteller
    Nicht verfügbar
    Verantwortliche Person
    CMN Printpool Inh. Mathis Neumann
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    Friedrich-Karl-Str. 9
    Hamburg
    Deutschland

    Weitere beliebte Ausgaben desselben Titels

    9783030713515: Data Science for Public Policy (Springer Series in the Data Sciences)

    Vorgestellte Ausgabe

    ISBN 10:  3030713512 ISBN 13:  9783030713515
    Verlag: Springer-Verlag GmbH, 2021
    Hardcover