{"product_id":"veridical-data-science-the-practice-of-responsible-data-analysis-and-decision-making-9780262049191","title":"Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making","description":"\u003cb\u003eUsing real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003eMost 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. \u003ci\u003eVeridical Data Science\u003c\/i\u003e, 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. \u003cbr\u003eBin 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, \u003ci\u003eVeridical Data Science\u003c\/i\u003e 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. \u003cp\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePresents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven results\u003c\/li\u003e\n\u003cli\u003eTeaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process\u003c\/li\u003e\n\u003cli\u003eCultivates critical thinking throughout the entire data science life cycle\u003c\/li\u003e\n\u003cli\u003eProvides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions\u003c\/li\u003e\n\u003cli\u003eSuitable for advanced undergraduate and graduate students, domain scientists, and practitioners\u003c\/li\u003e\n\u003c\/ul\u003e\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eBin Yu\u003c\/b\u003e 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. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eRebecca L. Barter\u003c\/b\u003e is Research Assistant Professor in Epidemiology at the University of Utah.\u003cbr\u003e","brand":"MIT Press","offers":[{"title":"Default Title","offer_id":50869345681682,"sku":"9780262049191","price":87.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_26232a87-8745-4167-bfe2-854c81e857b8.jpg?v=1737754615","url":"https:\/\/surprise-castle.myshopify.com\/products\/veridical-data-science-the-practice-of-responsible-data-analysis-and-decision-making-9780262049191","provider":"Surprise Castle","version":"1.0","type":"link"}