Discrete event data about dynamic behavior of individual actors or agents in larger systems is complex and hard to analyze. Process mining provides techniques to analyze discrete event data for understanding, improving, and predicting processes from various application domains in an objective and explainable way. 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. You learn how to analyze multi-dimensional event data (in databases), unstructured event data (in event logs), and (real-time) event streams. The course covers how to design model learning algorithms and how to evaluate and improve model quality to learn explainable behavioral models using detailed diagnostic information using pattern detection, graph structure analysis, and state-of-the-art stream data mining.