Veridical Data Science

<b>Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science.</b><br><br>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. <i>Veridical Data Science</i>, 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. <br>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, <i>Veridical Data Science</i> 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. <br><br><ul><li>Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven results</li><li>Teaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process</li><li>Cultivates critical thinking throughout the entire data science life cycle</li><li>Provides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions</li><li>Suitable for advanced undergraduate and graduate students, domain scientists, and practitioners</li></ul>