Data Science for Public Policy | Taschenbuch

Cover: 9783030713546 | Data Science for Public Policy | Jeffrey C. Chen (u. a.) | Taschenbuch

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Sprache: Englisch

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Beschreibung

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 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

Zusammenfassung

Combines anecdotes from public sector experience with technical aspects of field

Addresses current topics in ethics and fairness, data product development, and team organization in data science

Includes data sets and functioning code examples

Inhaltsverzeichnis

An Introduction.- The Case for Programming.- Elements of Programming.- Transforming Data.- Record Linkage.- Exploratory Data Analysis.- Regression Analysis.- Framing Classification.- Three Quantitative Perspectives.- Prediction.- Cluster Analysis.- Spatial Data.- Natural Language.- The Ethics of Data Science.- Developing Data Products.- Building Data Teams.- Appendix A: Planning a Data Product.- Appendix B: Interview Questions.

Erscheinungsjahr:	2022
Fachbereich:	Wahrscheinlichkeitstheorie
Genre:	Mathematik
Rubrik:	Naturwissenschaften & Technik
Medium:	Taschenbuch
Reihe:	Springer Series in the Data Sciences
Inhalt:	xiv 363 S. 12 s/w Illustr. 111 farbige Illustr. 363 p. 123 illus. 111 illus. in color.
ISBN-13:	9783030713546
ISBN-10:	3030713547
Sprache:	Englisch
Ausstattung / Beilage:	Paperback
Einband:	Kartoniert / Broschiert
Autor:	Chen, Jeffrey C. Cornwall, Gary J. Rubin, Edward A.
Auflage:	1st ed. 2021
Hersteller:	Springer International Publishing Springer International Publishing AG Springer Series in the Data Sciences
Maße:	279 x 210 x 21 mm
Von/Mit:	Jeffrey C. Chen (u. a.)
Erscheinungsdatum:	02.09.2022
Gewicht:	0,924 kg

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