A Manager’s Guide to Business Analytics and Machine Learning with AI

Mark Keith, PhD Thomas Meservy, PhD

Producing an analysis has become cheap, but judging one has not. This resource prepares managers for the part of analytics that remains theirs: framing consequential business questions, directing AI-assisted analysis, and deciding whether a result deserves action. Students use AI to generate Python code but learn enough to read it, test it, and spot problems like data leakage and misleading charts. Organized around CRISP-DM, the resource moves from data exploration and cleaning through regression and classification to deployment, strategy, and responsible analytics, ending in a capstone project. Students leave able to brief an analyst or AI assistant, explain what was tested, and defend a decision to a skeptical executive audience.