Data Science for Business

Written for the people who work with data scientists rather than for the people who will build the models. It teaches the fundamental concepts, including how a business problem maps onto a data mining task, why overfitting happens and how holdout evaluation guards against it, and how similarity, clustering and probability estimation actually work. The treatment of evaluation is unusually good, covering expected value calculations, lift and profit curves, and why accuracy is the wrong measure when classes are unbalanced. Worked examples run through churn prediction and targeted marketing from framing to business case. There is enough maths to follow the reasoning and not so much that it becomes a textbook.