Data science for public policy / (Record no. 22798)

MARC details
000 -LEADER
fixed length control field 02097nam a22002297a 4500
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
ISBN 9783030713515
041 ## - LANGUAGE CODE
Language code of text/sound track or separate title English
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 300.285
Item number CHE
100 ## - MAIN ENTRY--AUTHOR NAME
Author name Chen, Jeffrey
245 ## - TITLE STATEMENT
Title Data science for public policy /
Statement of responsibility, etc Jeffrey Chen ,Edward A. Rubin , Gary J. Cornwall
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication Cham, Switzerland :
Name of publisher Springer,
Year of publication 2021.
300 ## - PHYSICAL DESCRIPTION
Number of Pages xiv, 363 pages :
Other physical details illustrations (some color), maps ;
Dimensions 29 cm
440 ## - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Springer series in the data sciences
505 ## - FORMATTED CONTENTS NOTE
Formatted contents note 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 ## - SUMMARY, ETC.
Summary, etc 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<br/>
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Policy sciences
General subdivision Data processing
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Computer science
General subdivision Mathematics
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Subject Statistics
700 ## - ADDED ENTRY--PERSONAL NAME
Author 2/ Editor Rubin, Edward A.
700 ## - ADDED ENTRY--PERSONAL NAME
Author 2/ Editor Cornwall, Gary J.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Dewey Decimal Classification
Koha item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Use restrictions Not for loan Collection code Home library Current library Shelving location Date acquired Source of acquisition Cost, normal purchase price Bill Date Full call number Accession Number Price effective from Koha item type
    Dewey Decimal Classification   Restricted Access   Reference Institute of Public Enterprise, Library Institute of Public Enterprise, Library S Campus 12/31/2023 Overseas Press India 5959.58 09/11/2023 300.285 CHE 47882 12/31/2023 Books

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