Veridical Data Science

Name: Veridical Data Science
Brand: The MIT Press
SKU: 9780262049191-MTUSDTR
Price: 640.0 HKD
Availability: InStock

The Practice of Responsible Data Analysis and Decision Making

Bin Yu (Author) • Rebecca L. Barter (Author) ... more

Edition: US - Hardback

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English • Age Adult - Adult

526 pages • 6.25 x 9.31 x 1.41 in

Approx. weight: 2.09 lb

Publication date: 15 Oct,2024

Barcode: 9780262049191 • The MIT Press

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By: Bin Yu, Rebecca L. Barter | Series: Adaptive Computation and Machine Learning series

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