Data science for public policy / Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall.

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Data science for public policy / Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall.

9783030713522 (electronic bk.)
3030713520 (electronic bk.)
9783030713515
3030713512

Cham : Springer, [2021]

©2021

English

1 online resource : illustrations (chiefly color).

10.1007/978-3-030-71352-2 doi

H61.3 .C54 2021

300.285

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 analysts 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.

Includes bibliographical references and index.

Access limited to authorized users.

Online resource; title from PDF title page (SpringerLink, viewed September 13, 2021).

Springer series in the data sciences.

Print version: 9783030713515

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