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Public Policy Analytics

Code and Context for Data Science in Government

«

Finally, a book that connects two parallel lessons. First, how to design geospatial data science workflows in the public policy sector; and second, when applying algorithms in government, there is no free lunch. Rather than making data science in government a bleak challenge suited only the fearless, Ken sustains a tone of optimism and a sense of purpose for readers curious enough to ponder the kernels of wisdom he has sprinkled in each chapter.

-- Mark Wheeler, Chief Information Officer, City of Philadelphia

Public Policy Analytics is a must-read for creating data-driven urban plans and policies. In crisp and compelling chapters, Dr. Steif steps through real-world problems and links them to critical methods in R. The included assignments are perfect for both self-guided students and educators. There is no better guide to data science in the public realm!

-- Dr. Allison Lassiter, Assistant Professor of City & Regional Planning, Univ. of Pennsylvania

Ken Steif has written a clever and instructive text to guide students of planning and public policy decision-making. This accessible book brings data science and machine learning into the realm of public policy through a series of common and compelling “use cases,” with an emphasis on the critical role of geospatial analysis. The examples provided are practical, address important social issues, and demonstrate impact. Readers will appreciate the thoughtfulness of the prose and a narration sympathetic to the challenges of doing data science in a policy environment.

-- Dr. Dennis Culhane, Dana and Andrew Stone Professor of Social Policy, Univ. of Pennsylvania

»

Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand 'spatial process' and develop spatial analytics; how to develop 'useful' predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and 'Planning' are one and the same. Les mer

2085,-
Sendes innen 21 dager
Public Policy Analytics: Code & Context for Data Science in Government teaches readers how to address complex public policy problems with data and analytics using reproducible methods in R. Each of the eight chapters provides a detailed case study, showing readers: how to develop exploratory indicators; understand 'spatial process' and develop spatial analytics; how to develop 'useful' predictive analytics; how to convey these outputs to non-technical decision-makers through the medium of data visualization; and why, ultimately, data science and 'Planning' are one and the same. A graduate-level introduction to data science, this book will appeal to researchers and data scientists at the intersection of data analytics and public policy, as well as readers who wish to understand how algorithms will affect the future of government.

Detaljer

Forlag
CRC Press
Innbinding
Innbundet
Språk
Engelsk
Sider
228
ISBN
9780367516253
Utgivelsesår
2021
Format
25 x 18 cm

Anmeldelser

«

Finally, a book that connects two parallel lessons. First, how to design geospatial data science workflows in the public policy sector; and second, when applying algorithms in government, there is no free lunch. Rather than making data science in government a bleak challenge suited only the fearless, Ken sustains a tone of optimism and a sense of purpose for readers curious enough to ponder the kernels of wisdom he has sprinkled in each chapter.

-- Mark Wheeler, Chief Information Officer, City of Philadelphia

Public Policy Analytics is a must-read for creating data-driven urban plans and policies. In crisp and compelling chapters, Dr. Steif steps through real-world problems and links them to critical methods in R. The included assignments are perfect for both self-guided students and educators. There is no better guide to data science in the public realm!

-- Dr. Allison Lassiter, Assistant Professor of City & Regional Planning, Univ. of Pennsylvania

Ken Steif has written a clever and instructive text to guide students of planning and public policy decision-making. This accessible book brings data science and machine learning into the realm of public policy through a series of common and compelling “use cases,” with an emphasis on the critical role of geospatial analysis. The examples provided are practical, address important social issues, and demonstrate impact. Readers will appreciate the thoughtfulness of the prose and a narration sympathetic to the challenges of doing data science in a policy environment.

-- Dr. Dennis Culhane, Dana and Andrew Stone Professor of Social Policy, Univ. of Pennsylvania

»

«

Finally, a book that connects two parallel lessons. First, how to design geospatial data science workflows in the public policy sector; and second, when applying algorithms in government, there is no free lunch. Rather than making data science in government a bleak challenge suited only the fearless, Ken sustains a tone of optimism and a sense of purpose for readers curious enough to ponder the kernels of wisdom he has sprinkled in each chapter.

-- Mark Wheeler, Chief Information Officer, City of Philadelphia

Public Policy Analytics is a must-read for creating data-driven urban plans and policies. In crisp and compelling chapters, Dr. Steif steps through real-world problems and links them to critical methods in R. The included assignments are perfect for both self-guided students and educators. There is no better guide to data science in the public realm!

-- Dr. Allison Lassiter, Assistant Professor of City & Regional Planning, Univ. of Pennsylvania

Ken Steif has written a clever and instructive text to guide students of planning and public policy decision-making. This accessible book brings data science and machine learning into the realm of public policy through a series of common and compelling “use cases,” with an emphasis on the critical role of geospatial analysis. The examples provided are practical, address important social issues, and demonstrate impact. Readers will appreciate the thoughtfulness of the prose and a narration sympathetic to the challenges of doing data science in a policy environment.

-- Dr. Dennis Culhane, Dana and Andrew Stone Professor of Social Policy, Univ. of Pennsylvania

»

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