The focus of this chapter is on the generation and evaluation of association rules, which are popular methods for uncovering patterns in the records of events with a sequence component, for instance, attraction point visitations by a tourist, customer’s purchases during a grocery store visit, or a record of social media users’ engagements (“likes”). Association rules are applied in developing recommender systems, travel itinerary generation, digital footprint analysis, etc. The chapter introduces the basic measures of frequency and strength of associations between the analyzed items. Advanced metrics like leverage and conviction refine the assessment of rule interestingness, addressing the limitations of basic measures. The practical part of the chapter includes a hands-on lab using Python to analyze bakery transaction data, guiding readers through the sequence of essential data analysis steps: descriptive analysis, data visualization, rule generation, and results interpretation.