Data Science for Public Policy (Paperback, 1st ed. 2021)

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.

General

Imprint:	Springer Nature Switzerland AG
Country of origin:	Switzerland
Series:	Springer Series in the Data Sciences
Release date:	September 2022
First published:	2021
Authors:	Jeffrey C. Chen • Edward A. Rubin • Gary J. Cornwall
Dimensions:	279 x 210mm (L x W)
Format:	Paperback
Pages:	363
Edition:	1st ed. 2021
ISBN-13:	978-3-03-071354-6
Categories:	Books > Reference & Interdisciplinary > Communication studies > Data analysis Books > Social sciences > Politics & government > Public administration Books > Science & Mathematics > Mathematics > Numerical analysis Books > Computing & IT > Computer software packages > Other software packages > Mathematical & statistical software Promotions
LSN:	3-03-071354-7
Barcode:	9783030713546

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