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. This is primarily because (i) these courses frequently fail to frame the lectures around a real-world application; (ii) quantitative methods are typically illustrated with an unrealistically clean data set that fits the assumptions of the method in an equally unrealistic way. When students use this approach to solve problems in the real-world, they are unable to identify the most appropriate methodological approach when it is not spoon fed.
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 are developing the opencasestudies educational resource of case studies, which demonstrate illustrative data analyses that can be used in the classroom to teach students how to effectively derive knowledge from data. This approach has successfully been used to teach data science courses at many universities, including: