Foundations of Data and Artificial Intelligence provides a practical, accessible introduction to data, analytics, and AI for college-level courses and workforce training. Designed for full-semester use, the text blends conceptual foundations with hands-on learning through applied labs, real datasets, and guided exercises.
Students learn how data is created, cleaned, stored, analyzed, and used to train AI systems. The book introduces machine learning, supervised and unsupervised models, basic statistics, data visualization, and the fundamentals of responsible AI - including fairness, privacy, bias, and evaluation of AI outputs. Clear explanations and step-by-step activities support learners with diverse backgrounds.
Topics include data types, quality, cleaning, metadata, databases, cloud storage, APIs, R programming basics, machine learning fundamentals, real-world AI applications, statistics, visualization, prompt engineering, AI evaluation, ethics, and emerging risks such as bias and misinformation.
Instructor resources and learning materials are available from the publisher.
