Introduction to the CRISP-DM Methodology (Analytics & Data Science)

This video discusses the analytics lifecycle, focusing on the popular CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology. This five-phase cycle begins with “Business Understanding,” ensuring analytics address a specific business problem (e.g., optimizing pricing, segmenting customers). Next is “Data Understanding,” where data availability, quality, and granularity are assessed, often using a sandbox for exploration. The most time-consuming phase is “Data Preparation,” involving cleaning and scrubbing data for modeling. The “Modeling” phase entails building simplified representations of real-world systems (e.g., predicting car accidents) using tools like Python or R. Finally, “Evaluation and Deployment” assesses model effectiveness and prepares it for real-world use, emphasizing that models must be utilized after creation.
Source: Cody Baldwin