Open Case Studies
What is the Open Case Studies (OCS) project?
The Open Case Studies project is an educational resource of experiential guides that demonstrate how to effectively derive knowledge from data in real-world challenges.
All case studies can be found in our OCS search tool .
Our case studies can be used:
- by educators to help them teach
- by students to help them with their classes
- by independent learners to help them learn
Find out more about how others are using case studies and what they think of them in this study.
Statistical topics: from correlation to the influence of multicollinearity on linear regression
Data science topics: from making simple plots to building dashboards and performing machine learning (ML), a type of artificial intelligence (AI)!
What problem are we addressing?
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.
Problems with previously suggested solutions
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.
What are we proposing as a solution?
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:
