Hey everyone. I'm a CS student at FAST-NUCES in Karachi, and for the past 7 weeks I've been doing two things in parallel: the IBM Data Science Professional Certificate and an 8-week AI Engineering Internship that goes from Python fundamentals all the way to LLMs and deployment.
I wanted to write this partly to keep myself accountable, and partly because when I was starting out, reading other people's honest "here's what I actually struggled with" posts helped me a lot more than polished tutorials did.
So here's a real recap of the last 7 weeks — what I covered, what clicked, and what didn't.
Week 1 — Python for AI & Data Handling
Refreshed Python and got hands-on with NumPy, Pandas, and Matplotlib by building a Student Performance Analyzer on a class dataset. The interesting part wasn't the coding — it was seeing what the numbers actually said: pass rate came out to 93.3%, but the spread in Math scores was wide, clustering heavily at both the low (40s) and high (90s) ends rather than in the middle. Small reminder that "average" can hide a lot.
