This module represents the second essential part of a comprehensive course designed to guide learners through the complete data-centric pipeline of industrial process control. It offers a hands-on approach to mastering every stage, starting from the design of data acquisition systems for a specific process, to collecting, storing, and preprocessing the acquired data. Learners will then progress to using this data to train accurate and reliable process models, which can ultimately be employed to enhance the efficiency, reliability, and outcome of industrial operations. In this second part, the focus is on data exploitation using advanced machine learning techniques. Participants will be introduced to a range of methods, from traditional Statistical Process Control (SPC) approaches to modern AI-driven strategies. These include tools for detecting process non-conformities, identifying patterns, and classifying operational status to support informed decision-making. By combining theory with practice, learners will gain a robust understanding of how data can be transformed into actionable insights for process improvement. Overall, this engaging learning experience equips future industry professionals with a formidable mix of knowledge and technical skills that are highly valued in the evolving field of industrial automation and data analytics.