An increase in demand for gateway training in statistics, biostatistics, and data science has led to changes in curriculum, specifically an increase in computing. While this has led to more applied courses, students still struggle with effectively deriving knowledge from data, because they frequently fail to frame the lectures around a real-world application. In 1999, Nolan and Speed argued the solution was to teach courses through in-depth case studies derived from interesting problems, with nontrivial solutions that leave room for different analyses. This innovative framework teaches the student to make important connections between the scientific question, data and statistical concepts that only come from hands-on experience analyzing data. However, these case studies based on realistic challenges, not toy examples, are scarce. To address this, we have developed the openCaseStudies educational resource, which demonstrates illustrative data analyses that can be used in the classroom to teach students how to effectively derive knowledge from data. This resource is scalable and sustainable with templates that can be used to develop new case studies from outside contributors.