September 12, 2025

My Tools in Data Science course uses LLMs for assessments. We use LLMs to Suggest project ideas (I pick), e.g. https://chatgpt.com/share/6741d870-73f4-800c-a741-af127d20eec7 Draft the project brief (we edit), e.g. https://docs.google.com/document/d/1VgtVtypnVyPWiXied5q0_CcAt3zufOdFwIhvDDCmPXk/edit Propose scoring rubrics (we tweak), e.g. https://chatgpt.com/share/68b8eef6-60ec-800c-8b10-cfff1a571590 Score code against the rubric (we test), e.g. https://github.com/sanand0/tds-evals/blob/5cfabf09c21c2884623e0774eae9a01db212c76a/llm-browser-agent/process_submissions.py Analyze the results (we refine), e.g. https://chatgpt.com/share/68b8f962-16a4-800c-84ff-fb9e3f0c779a This changed our assessments process. It’s easier and better. Earlier, TAs took 2 weeks to evaluate 500 code submissions. In the example above, it took 2 hours. Quality held up: LLMs match my judgement as closely as TAs do but run fast and at scale. ...

Slides for my DataHack Summit talk (controversially) titled RIP Data Scientists are at https://sanand0.github.io/talks/2025-08-21-rip-data-scientists/ Summary: as data scientists we explore, clean, model, explain, deploy, and anonymize datasets. I live-vibe-coded each step with DGCA data in 35 minutes using ChatGPT. Of course, it’s the tasks that are dying, not the role. Data scientists will leverage AI, differentiate on other skills, and move on. But the highlight was an audience comment: “I’m no data scientist. I’m a domain person. I’ll tell you all this: If you don’t follow these practices, you won’t have a job with me!” ...