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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.

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Verlag
Springer
Erscheinungsdatum
2022
Sprache
Englisch
ISBN 10 
3030713547
ISBN 13 
9783030713546
Einband
Taschenbuch
Auflage
1
Anzahl der Seiten
380
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