Description
Data Science for Business introduces the principles of data-driven decision-making and the analytical thinking required to extract value from business data. Foster Provost and Tom Fawcett explain how data mining and predictive analytics can be applied to identify patterns, make predictions, evaluate risks, and support business decisions. The book develops concepts such as predictive modeling, classification, regression, similarity, clustering, model evaluation, and data-driven decision analysis while emphasizing the business questions that analytical methods are intended to answer. Rather than focusing primarily on programming, it explains how managers and analysts can think critically about data, analytical models, and their practical value. It provides a strong foundation for students and professionals interested in data science, business analytics, data mining, and evidence-based decision-making.
