Veridical Data Science (Hardcover)

Bin Yu

ISBN 10: 0262049198 / ISBN 13: 9780262049191

Published by MIT Press Ltd, 2024

About this Item

Hardcover. 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 resultsTeaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making processCultivates critical thinking throughout the entire data science life cycleProvides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutionsSuitable for advanced undergraduate and graduate students, domain scientists, and practitioners "Veridical Data Science gives a complete and comprehensive overview of the world of data science using a problem/solution-oriented prospective"-- Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9780262049191

Bibliographic Details

• Title: Veridical Data Science (Hardcover)
• Publisher: MIT Press Ltd
• Publication Date: 2024
• Language: English
• ISBN 10: 0262049198
• ISBN 13: 9780262049191
• Binding: Hardcover
• Condition: new

About this title

Synopsis

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

About the Author

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.