The Open Case Studies project is an educational resource that educators can use in the classroom to teach students how to effectively derive knowledge from data in real-world challenges.
Despite unprecedented and growing interest in data science on campuses, there are few courses and course materials that provide meaningful opportunity for students to learn about real-world challenges. Most courses frequently fail to frame the lectures around a real-world application and provide unrealistically clean datasets that fit the assumptions of the methods in an unrealistic way. The result is that students are left unable to effectively analyze data and solve real-world challenges outside of the classroom.
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 Open Case Studies 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:
Find the right open case study for you!
Addressing Public Health Challenges through Data Science Education. Hicks, Stephanie. American Public Health Association Annual Meeting. 2019 Nov 2-6. Philadelphia, PA, USA.
Motivating Data Science through Case Studies in Public Health. Eastern North American Region (ENAR) Meeting of the International Biometric Society. Jager, Leah. 2019 March 27. Philadelphia, PA, USA.