LLM-generated summary
Thomas Nield’s Essential Math for Data Science reviews the calculus, probability, statistics, and linear algebra that sit under everyday data work, then connects them to linear and logistic regression and related machine-learning practice. It is a practitioner-oriented refresher with code, not a proof-heavy mathematics text. The aim is to make model-fitting and evaluation less of a black box for people who already write some code.