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Data Science for Public Policy (Paperback, 1st ed. 2021): Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall Data Science for Public Policy (Paperback, 1st ed. 2021)
Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall
R1,563 Discovery Miles 15 630 Ships in 10 - 15 working days

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.

Data Science for Public Policy (Hardcover, 1st ed. 2021): Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall Data Science for Public Policy (Hardcover, 1st ed. 2021)
Jeffrey C. Chen, Edward A. Rubin, Gary J. Cornwall
R1,648 R1,553 Discovery Miles 15 530 Save R95 (6%) Ships in 9 - 15 working days

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.

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