Practical LLMs for Modern Data Science

Learn how large language models (LLMs) can be used as practical assistants in everyday data science work. Start by seeing LLM-generated code in action and then build your own reusable helpers that speed up repetitive tasks such as exploration, preprocessing, and boilerplate modeling. Find out how to use LLMs to generate code that can be inspected, executed, and validated, rather than trusting black-box outputs. Explore how LLMs behave in classification and regression tasks and how to evaluate those results using standard data science metrics.