000			02097nam a22002297a 4500
020			_a9783030713515
041			_aENG
082			_a300.285 _bCHE
100			_aChen, Jeffrey
245			_aData science for public policy / _cJeffrey Chen ,Edward A. Rubin , Gary J. Cornwall
260			_aCham, Switzerland : _bSpringer, _c2021.
300			_a xiv, 363 pages : _b illustrations (some color), maps ; _c29 cm
440			_aSpringer series in the data sciences
505			_aAn 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			_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 _xData processing
650			_aComputer science _xMathematics
650			_aStatistics
700			_aRubin, Edward A.
700			_aCornwall, Gary J.
942			_2ddc _cBK
999			_c22798 _d22798