000			a
999			_c30960 _d30960
008			220610b xxu||||| |||| 00| 0 eng d
020			_a9783030713515
082			_a300.285 _bCHE
100			_aChen, Jeffrey C.
245			_aData science for public policy
260			_bSpringer, _c2021 _aCham :
300			_aXIV, 363 p ; _bill., _c29 cm
365			_b64.99 _cEUR _d86.00
490			_aSpringer Series in the Data Sciences
504			_aIncludes bibliographical references and index.
520			_aThis 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.
650			_aPolicy sciences
650			_aData processing
650			_aStatistics
650			_aComputer science
650			_aMathematics
700			_aRubin, Edward A.
700			_aCornwall, Gary J.
942			_2ddc _cBK