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

Clinical Decision-Making in Oral Medicine (1版)

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Accurately diagnosing the myriad of diseases that can affect the mouth, guiding patients correctly, and offering treatment with competence is a permanent challenge for dental surgeons and health professionals. Often, the diagnosis of these diseases is neglected or wrongly performed. This concise book guides health professionals in clinical decision making in oral diseases, assisting them not only on the diagnosis and treatment of oral and maxillofacial diseases, but also in terms of value and benefit of who treats the condition and, above all, who will be treated. The work provides guidelines formulated by a compilation of specific protocols of conduct, focusing on the scientific evidence and enriched by the authors’ critical analyses. The book is divided into eight main sections that include twenty-eight chapters. Most of them have no more than 5 pages and follow the same format to standardize their use. Each chapter begins with a summary of the topic, a brief description of the main disease that fall into this diagnostic category, and their main definitions. Then, the clinical characteristics, ways of making the diagnosis, and a protocol for treatment for each lesion are presented. Additionally, the experienced authors interestingly discuss their perceptions on how to give bad news in the Oral Medicine setting; and the last section is especially dedicated to the dental management of medically complex patients or with little-known diseases, such as Burning Mouth Syndrome and COVID-19. More than 140 high resolution clinical images illustrate the book. Clinical Decision-Making in Oral Medicine targets is designed for health professionals and physicians from various specialties, who are interested in diseases with systemic repercussions that affect the oral and maxillofacial complex and in oral repercussions of treatments for complex diseases such as head and neck cancer. Students in dentistry may also find the book of value.