Descriptive vs. predictive analytics in an industrial context, KPI dashboard design, basic statistical analysis for process data, and how analytics ties back into Six Sigma and SPC.
Every method in this program runs on data, and the quality of the decisions those methods produce is bounded by how well that data is actually collected, summarized, and interpreted. This closing technical module covers the distinction between descriptive analytics (what already happened) and predictive analytics (what's likely to happen next), how to design a KPI dashboard that actually gets used rather than ignored, the basic statistical tools for making sense of process data, and — critically — why correlation between two process variables is never, by itself, evidence that one causes the other.
By the end of this module you should be able to explain why a control chart embedded in a live operator dashboard is a fundamentally more useful analytics tool than the same chart computed manually at the end of a shift — the exact idea this program's real-project case studies and capstone put into practice next.