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

Libro 7 de 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)
Tapa blanda
ISBN 10: 3030713547 ISBN 13: 9783030713546
Editorial: Springer, 2022

Sinopsis

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.

"Sinopsis" puede pertenecer a otra edición de este libro.

Acerca del 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

    De la contraportada

    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.

    "Sobre este título" puede pertenecer a otra edición de este libro.

    Editorial
    Springer
    Año de publicación
    2022
    Idioma
    Inglés
    ISBN 10 
    3030713547
    ISBN 13 
    9783030713546
    Encuadernación
    Tapa blanda
    Número de edición
    1
    Número de páginas
    380
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