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.
Our case studies can be used:
Find out more about how others are using case studies and what they think of them in this study.
Want to use Open Case Studies for teaching?
Learn how to
use our case studies and more about the OCS project using our
guide:
Website
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PDF
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Our case studies cover many concepts!
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)!
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:
Carrie Wright, Qier Meng, Michael R. Breshock, Lyla Atta, Margaret A. Taub, Leah R. Jager, John Muschelli & Stephanie C. Hicks
Open Case Studies: Statistics and Data Science Education through Real-World Applications
https://www.tandfonline.com/doi/full/10.1080/26939169.2024.2394541
Open Case Studies spans two complementary project areas, each grounded in real-world data and designed for hands-on learning. Through these projects, learners gain experience with data wrangling, visualization, and statistical analysis using R — applied to pressing challenges in public health and the biomedical sciences. Each case study is freely available and built to be used in the classroom or independently.
Open case studies on public health topics, developed in partnership with the Bloomberg American Health Initiative at Johns Hopkins.
Open case studies for the biomedical sciences, bringing real-world quantitative biology education to students, instructors, and independent learners.