國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Data science for public policy

Chen, Jeffrey C.

Data science for public policy

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science for public policyby Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall.
作者:	Chen, Jeffrey C.
其他作者:	Rubin, Edward A.
出版者:	Cham :Springer International Publishing :2021.
面頁冊數:	xiv, 363 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Policy sciencesData processing.
電子資源:	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:

905495
Policy sciences
--Data processing.

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

Dewey Class. No.: 300.285

Data science for public policy
LDR:02473nmm a2200337 a 4500 001 608177
003 DE-He213
005 20210901004940.0
006 m d
007 cr nn 008maaau
008 220119s2021 sz s 0 eng d
020 $a 9783030713522 $q (electronic bk.)
020 $a 9783030713515 $q (paper)
024 7 $a 10.1007/978-3-030-71352-2 $2 doi
035 $a 978-3-030-71352-2
040 $a GP $c GP
041 0 $a eng
050 4 $a H61.3 $b .C54 2021
072 7 $a PBKS $2 bicssc
072 7 $a MAT006000 $2 bisacsh
072 7 $a PBKS $2 thema
082 0 4 $a 300.285 $2 23
090 $a H61.3 $b .C518 2021
100 1 $a Chen, Jeffrey C. $3 905492
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 905495
650 1 4 $a Computational Mathematics and Numerical Analysis. $3 274020
650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
700 1 $a Rubin, Edward A. $3 905493
700 1 $a Cornwall, Gary J. $3 905494
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Springer series in the data sciences. $3 732743
856 4 0 $u https://doi.org/10.1007/978-3-030-71352-2
950 $a Mathematics and Statistics (SpringerNature-11649)