Making Hard Decisions with DecisionTools (3版)

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【簡介】 MAKING HARD DECISIONS WITH DECISIONTOOLS is a new edition of Bob Clemen's best-selling title, MAKING HARD DECISIONS. This straightforward book teaches the fundamental ideas of decision analysis, without an overly technical explanation of the mathematics used in decision analysis. This new version incorporates and implements the powerful DecisionTools software by Palisade Corporation, the world's leading toolkit for risk and decision analysis. At the end of each chapter, topics are illustrated with step-by-step instructions for DecisionTools. This new version makes the text more useful and relevant to students in business and engineering. 【目錄】

Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (1版)

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【簡介】 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