Discover trustworthy data science with “Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making.” This essential textbook moves beyond linear processes, offering an actionable framework for real-world data challenges. Learn to navigate ambiguous questions and messy data using the innovative Predictability, Computability, and Stability (PCS) framework. Authors Bin Yu and Rebecca Barter guide you through assessing data trustworthiness and relevance, addressing uncertainty from human decisions in data collection, cleaning, and modeling. Packed with real-world case studies, intuitive explanations of statistical and machine learning techniques, and R/Python code, this book provides a clear, accessible foundation for students and practitioners. Cultivate critical thinking and master responsible data analysis.