000			01583nam a2200217 4500
020			_a9783030713515
020			_a9783030713515
020			_a 9783030713522
082			_a006.3 _bCHE
084			_2Colon Classification
100			_aChen, Jeffrey C.
245			_aData Science for Public Policy/ _c Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall
260			_aSwitzerland: _bSpringer, _c2021.
300			_axiv, 363 p.
490			_aSpringer Series in the Data Sciences
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 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			_aData Science
700			_aRubin, Edward A.
700			_aCornwall, Gary J.
942			_2ddc _cBK
999			_c729120 _d729120