All About School - The Complete Education Forum and Classifieds

I’ve been exploring Python and Generative AI and I’m trying to understand how these areas fit together for someone who is still learning the fundamentals.

Python seems useful for working with datasets, automation and AI APIs, while Data Science adds areas such as statistics, data cleaning, analysis and visualization. Generative AI then opens up another set of applications involving language models, prompt design and AI-powered tools.

For someone starting with Data Science with Python, would it be better to learn Python, NumPy, Pandas, SQL and basic statistics before moving into machine learning and GenAI?

I also see many tutorials that jump directly into LLM APIs and prompt engineering. That can be useful for building quick prototypes, but I wonder whether understanding the underlying data and machine-learning concepts makes a significant difference in the long term.

I’d be interested to hear how others approached this. Did you learn the fundamentals first and then move into GenAI, or did you start building AI projects immediately and learn the theory along the way