Articoli correlati a Data Science for Public Policy

9783030713546: Data Science for Public Policy
Brossura
ISBN 10: 3030713547 ISBN 13: 9783030713546
Casa editrice: Springer, 2022

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Informazioni sull'autore

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

Dalla quarta di copertina

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.

Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.

Editore
Springer
Data di pubblicazione
2022
Lingua
Inglese
ISBN 10 
3030713547
ISBN 13 
9783030713546
Rilegatura
Copertina flessibile
Numero edizione
1
Numero di pagine
380
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