An Introduction to Data Science with Python

Miller’s text distinguishes itself by blending statistical understanding with applied programming, showing readers how to move fluidly between conceptual reasoning and Python-based execution. It’s structured like a guided workshop, with exercises that simulate authentic analytical workflows - importing data, cleaning it, building models, and interpreting results. The writing style is academic yet approachable, making it well-suited for learners transitioning from statistics, business, or computer science backgrounds. While it may cover familiar ground for experienced programmers, its integration of data pipelines and predictive modeling frameworks gives it significant long-term value. As an introductory textbook, it offers a complete picture of the data science process without overwhelming newcomers with jargon.

Thomas W. Miller is a faculty member at Northwestern University and managing director of the university’s Data Science program. His expertise spans marketing analytics, machine learning, and applied statistics, and he has authored several books that connect theoretical data concepts with hands-on computation.