Many real-life phenomena studied with Data Science methods unfold over time. They often involve many people, objectes, agents, machines, entites, etc. that interact with each other while distributed in time and space. Such dynamics are called processes and are present everywhere: in software systems medical treatments, logistics systems, manufacturing, and even entire organizations. Process mining combines recording and analysis of event data with advanced behavioral modeling techniques for highly understandable and explainable models for understanding, predicting, and improving processes of all kinds. The first part of this advanced course on process mining teaches students theoretical foundations and the state-of-the-art in process mining along a complete process mining methodology. In the second part you can choose one of multiple specialization tracks on analyzing multi-dimensional event data (in databases), unstructured event data (in event logs), and (real-time) event streams. Both parts cover how to design model learning algorithms and how to evaluate and improve model quality to learn explainable behavioral models using detailed diagnostic information.