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Data Science for Public Policy

Springer Series in the Data Sciences

Erschienen am 01.09.2021, Auflage: 1/2021
69,54 €
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In den Warenkorb
Bibliografische Daten
ISBN/EAN: 9783030713515
Sprache: Englisch
Umfang: xiv, 363 S., 12 s/w Illustr., 111 farbige Illustr.
Format (T/L/B): 2.2 x 28.5 x 21.5 cm
Einband: gebundenes Buch

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.

Autorenportrait

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