Data engineering, ML pipelines, analytics, visualisation and Python tooling.
Everyone tells beginners to start with Python. After four years in data roles, I'd tell them to start with SQL instead. Here's why it matters more, transfers more broadly, and takes less time to become useful.
Building my first real machine learning model, I was thrilled when it hit 99% accuracy on the test set. Then I deployed it and discovered why that number meant almost nothing.
I wrote Pandas code that worked but was slow, verbose, and embarrassing to show other data scientists. Here are the specific patterns I was using and the better alternatives that changed my approach.
I've built dozens of dashboards and watched most of them get ignored within a month. Here's what I learned about the difference between dashboards that inform decisions and dashboards that just exist.
I was an advanced Excel user who kept hearing I should learn Python. Here's the specific 90-day path I took, what resources actually worked, and the moment it stopped feeling like learning and started feeling like a tool.
Step-by-step guide to building a production-ready RAG pipeline using LangChain, GitHub for version control, and modern best practices for chunking, embedding, retrieval, and evaluation.