Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making

Author:   Bin Yu ,  Rebecca L. Barter
Publisher:   MIT Press Ltd
ISBN:  

9780262049191


Pages:   526
Publication Date:   15 October 2024
Format:   Hardback
Availability:   To order   Availability explained
Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us.

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Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making


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Overview

Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle- the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven results Teaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process Cultivates critical thinking throughout the entire data science life cycle Provides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions Suitable for advanced undergraduate and graduate students, domain scientists, and practitioners

Full Product Details

Author:   Bin Yu ,  Rebecca L. Barter
Publisher:   MIT Press Ltd
Imprint:   MIT Press
Weight:   0.567kg
ISBN:  

9780262049191


ISBN 10:   0262049198
Pages:   526
Publication Date:   15 October 2024
Audience:   General/trade ,  General
Format:   Hardback
Publisher's Status:   Active
Availability:   To order   Availability explained
Stock availability from the supplier is unknown. We will order it for you and ship this item to you once it is received by us.

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Bin Yu is Chancellor's Distinguished Professor and Class of 1936 Second Chair in Statistics, EECS, and Computational Biology at the University of California, Berkeley, a 2006 Guggenheim Fellow, and a member of the US National Academy of Sciences and the American Academy of Arts and Sciences. Rebecca L. Barter is Research Assistant Professor in Epidemiology at the University of Utah.

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