Data Science for Public Policy

This textbook provides data science tools and concepts to public officials, policy analysts, and economists for public sector applications, emphasizing data-driven practices and causal inference/prediction techniques. It includes open-sourced data and live code in R notebook form for readers to practice.

Format: Hardback
Length: 363 pages
Publication date: 01 September 2021
Publisher: Springer Nature Switzerland AG

This comprehensive textbook serves as a vital resource for public officials, policy analysts, and economists, among others, in advancing their understanding and application of data science in the public sector. Expanding upon the quantitative economics frameworks commonly taught in policy and business schools, this book emphasizes the critical role of asking relevant questions to inform public policy. It employs a data-driven approach, starting with fundamental programming paradigms that occupy a significant portion of an analyst's time and progressing to practical applications of statistical learning and machine learning. By incorporating techniques from both causal inference and prediction, the text offers a multifaceted perspective to support its applications. Furthermore, the book includes open-sourced data and live code, written in the R programming language and presented in notebook format, allowing readers to engage with and modify the material to gain hands-on experience in working with data.

The textbook begins by introducing the fundamental tools and concepts of data science, including statistical analysis, data visualization, and machine learning. It then delves into the process of asking relevant questions and gathering data to inform public policy. The book emphasizes the importance of data quality, integrity, and accessibility, and discusses strategies for collecting and analyzing data in a responsible and ethical manner.

One of the key strengths of the textbook is its emphasis on practical applications. It provides examples and case studies from various fields, such as healthcare, education, and public safety, to illustrate how data science can be used to address real-world problems. The book also includes open-sourced data and live code, allowing readers to apply the techniques and concepts discussed in the text to their own data sets.

In addition to its practical focus, the textbook also addresses two divergent, competing perspectives to support its applications. The first perspective, known as causal inference, focuses on understanding the relationships between variables and using this understanding to make predictions. The second perspective, known as prediction, focuses on making predictions using data without necessarily understanding the underlying relationships.

The book incorporates techniques from both causal inference and prediction to provide a comprehensive understanding of data science. It discusses the advantages and limitations of each perspective and provides guidance on how to choose the appropriate approach for different situations. The book also emphasizes the importance of transparency and accountability in data science, and discusses strategies for communicating results and ensuring that data is used in a responsible and ethical manner.

Overall, this textbook is an essential resource for anyone interested in advancing their understanding and application of data science in the public sector. Its comprehensive coverage of the field, practical applications, and emphasis on transparency and accountability make it a valuable tool for policymakers, analysts, and economists alike.

Weight: 1220g
Dimension: 217 x 287 x 28 (mm)
ISBN-13: 9783030713515
Edition number: 1st ed. 2021