Preparing Data for Analytics and AI teaches students how to clean, structure, and prepare real-world data using SQL, R, and Python. Learners develop practical skills in data profiling, handling messy values, joins and merges, filtering, derived fields, reshaping tables, and maintaining data quality at scale. The text emphasizes reproducible, automated, and AI-assisted workflows that transform raw data into reliable, analysis-ready datasets.
Designed for college courses and workforce programs, the book blends conceptual explanations with hands-on labs, real datasets, and guided exercises. Clear, accessible writing supports learners from any discipline and prepares them for modern analytics and AI pipelines.
Topics include SQL wrangling, data quality checks, metadata, restructuring, automation, R and Python prep workflows, and AI-assisted data cleaning. Instructor resources are available from the publisher.
