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Data science for public policy

Chen, Jeffrey C.

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Data science for public policy

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data science for public policy/ by Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall.
Author:	Chen, Jeffrey C.
other author:	Rubin, Edward A.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xiv, 363 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer Nature eBook
Subject:	Policy sciences - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-030-71352-2
ISBN:	9783030713522

Data science for public policy
Chen, Jeffrey C.

Data science for public policy[electronic resource] /by Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall. - Cham :Springer International Publishing :2021. - xiv, 363 p. :ill., digital ;24 cm. - Springer series in the data sciences,2365-5682. - Springer series in the data sciences..

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.

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.

ISBN: 9783030713522

Standard No.: 10.1007/978-3-030-71352-2doiSubjects--Topical Terms:

3514739
Policy sciences
--Data processing.

LC Class. No.: H61.3 / .C54 2021

Dewey Class. No.: 300.285

Data science for public policy
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245 1 0 $a Data science for public policy $h [electronic resource] / $c by Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xiv, 363 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in the data sciences, $x 2365-5682
505 0 $a 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.
520 $a 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.
650 0 $a Policy sciences $x Data processing. $3 3514739
650 1 4 $a Computational Mathematics and Numerical Analysis. $3 891040
650 2 4 $a Statistics and Computing/Statistics Programs. $3 894293
700 1 $a Rubin, Edward A. $3 3514737
700 1 $a Cornwall, Gary J. $3 3514738
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