Ruobin Gong, Rutgers University, was one of the members of a panel at last year’s JSM, which discussed the teaching of data science using the Veridical Data Science framework developed by Bin Yu and Rebecca L. Barter. Ruobin writes:
Data Science in the Classroom: Let’s Get Real
Do you feel overwhelmed by the shape-shifting challenges in data science and look for ways to effectuate change in the classroom? Rest assured that you are not alone. In a recent panel interview with the Journal of Statistics and Data Science Education, five educators shared their own struggles and revelations.
The participants were Matteo Bonvini (Rutgers University, New Brunswick), Andrew Bray (University of California, Berkeley), Ruobin Gong (Rutgers University, New Brunswick), and Bin Yu (University of California, Berkeley); it was moderated by Joshua Rosenberg (University of Tennessee, Knoxville).
The interview is an extended conversation among the panelists on teaching data science using the Veridical Data Science (VDS) framework, which builds on the panel that took place during the 2025 Joint Statistical Meeting in Nashville, TN [you may recall Ruobin wrote about this in her “Sound the Gong” column in the December 2025 issue: https://imstat.org/2025/11/15/sound-the-gong-data-science-realism/).
Among the things discussed in the interview are the panelists’ distinct personal paths to data science, the evolving pedagogical objectives of data science education, the unique values added by the VDS framework, as well as how it meshes with the modern reality in which LLM-based assistance becomes indispensable.
To read the whole interview, visit https://www.tandfonline.com/doi/full/10.1080/26939169.2026.2632565
Topics covered in the interview:
1. The courses where we use Veridical Data Science (VDS)
2. How we came to adopt VDS in our classrooms
3. Our personal paths to Data Science
4. Goal of Data Science education
5. Comparing Data Science and Statistics
6. Challenges in teaching Data Science
7. What do we want students entering the class we teach to know
8. Teaching Data Science with VDS
9. Challenges brought by LLMs When shifting a Data Science class to one that is VDS-flavored
10. Where does VDS stand in the landscape of Data Science frameworks?
11. GAISE and Data Science
12. More on the experience of Data Science in K–12
13. On the Data Science education literature