I'm teaching "Data Science" to computer science majors next spring, and I'm overwhelmed by the prospect of what to teach them in the age of AI. It seems like Fable already way outperforms anything I could historically expect my students to do at the end of the semester, and perhaps even outperforms me. I can describe a vague analysis on the data, and it will handle every step: transformation, preliminary analysis, formulating more precise questions, creating visualizations, running statistical tests, interpreting results, thinking about stakeholders... And in some of those steps, it finds things that I might not have considered.

I wish I had a bigger N so I could rely on that experience a bit more, but it sure does feel like I'm not really contributing anything in terms of "Data Science expertise" in my own research right now.

did you find tricks to turn LLMs into super-assistants so students can learn deeper ?

i also wonder if they're not losing motivation due to the fact that the llm can race through everything so swiftly

From my experience working with data scientists, it outperforms humans on an extreme level. I’ve seen so much bad data science, and here I have a model that can do all of that from one prompt. It is getting really good on finding caveats in data too.