Data Science in Context by Alfred Z Spector
Data science is the foundation of our modern world. It underlies applications used by billions of people every day, providing new tools, forms of entertainment, economic growth, and potential solutions to difficult, complex problems. These opportunities come with significant societal consequences, raising fundamental questions about issues such as data quality, fairness, privacy, and causation. In this book, four leading experts convey the excitement and promise of data science and examine the major challenges in gaining its benefits and mitigating its harms. They offer frameworks for critically evaluating the ingredients and the ethical considerations needed to apply data science productively, illustrated by extensive application examples. The authors' far-ranging exploration of these complex issues will stimulate data science practitioners and students, as well as humanists, social scientists, scientists, and policy makers, to study and debate how data science can be used more effectively and more ethically to better our world.
'This book provides an important view of the contextual landscape for data science: the context of related fields of statistics, visualization, optimization, and computer science; the context of a broad range of applications, together with an analysis rubric; the context of societal impacts from dependability, to understandability, to ethical and legal questionsThese are critically important factors for any practitioner of data science to understand, and for others to be aware of in evaluating the use of data science.' Daniel Huttenlocher, Massachusetts Institute of Technology
'As data science becomes a crucial element in momentous decisions of war and peace, as well as commerce and innovation, it is vital that it rests on sound foundations. This book is an important step forward in that regard, illuminating the context in which data science is practiced. It is essential reading for both data scientists and decision makers.' James Arroyo, Ditchley Foundation
'Data science touches every aspect of our modern lives. This book digs into the practical, legal, and ethical challenges that result. It is the only book that's comprehensive in its consideration of the thorny issues arising from the broad application and unprecedented growth of data science. If you do data science, you should read this book.' Michael D. Smith, Harvard University
'This book will be essential reading for all data scientists and data teams. The self-contained text explains what students and practitioners need to know to use data science more effectively and ethically. It draws on the authors' years of experience and offers practical insights into data science that complement other books that focus on specific techniques. I'll be referencing and recommending this book for many years to come.' Ben Lorica, Gradient Flow
'As data science becomes a crucial element in momentous decisions of war and peace, as well as commerce and innovation, it is vital that it rests on sound foundations. This book is an important step forward in that regard, illuminating the context in which data science is practiced. It is essential reading for both data scientists and decision makers.' James Arroyo, Ditchley Foundation
'Data science touches every aspect of our modern lives. This book digs into the practical, legal, and ethical challenges that result. It is the only book that's comprehensive in its consideration of the thorny issues arising from the broad application and unprecedented growth of data science. If you do data science, you should read this book.' Michael D. Smith, Harvard University
'This book will be essential reading for all data scientists and data teams. The self-contained text explains what students and practitioners need to know to use data science more effectively and ethically. It draws on the authors' years of experience and offers practical insights into data science that complement other books that focus on specific techniques. I'll be referencing and recommending this book for many years to come.' Ben Lorica, Gradient Flow
Kay, Martin: - Martin Kay is professor of linguistics at Stanford University and an honorary professor at the University of Saarland, Germany. He is a past president of the Association for the Computational Linguistics and resigned the chairmanship of the International Committee on Computational Linguistics in 2014, having served in that capacity for thirty years.
| SKU | Unavailable |
| ISBN 13 | 9781009272209 |
| ISBN 10 | 1009272209 |
| Title | Data Science in Context |
| Author | Alfred Z Spector |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Cambridge University Press |
| Year published | 2022-10-20 |
| Number of pages | 335 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |