A manager's guide to the core statistical toolkit — descriptive statistics, probability distributions, sampling, hypothesis testing, confidence intervals, and regression — used to make data-driven business decisions.
How predictive modeling works in business analytics: regression, decision trees, random forests, and neural networks, with a worked Python example and guidance on evaluating and validating models.
How to choose the right chart type, build effective dashboards, and avoid common visualization pitfalls — with retail, financial services, and healthcare examples of interpreting business data visually.
A guide to data mining in business analytics covering descriptive, predictive, and prescriptive analytics, clustering, classification, and association rule learning, with a Python illustration and the CRISP-DM process.