In this video, I break down why so many self-taught programmers get stuck (or quit) and how you can avoid the exact mistakes I made on my own journey from self-taught data engineer to data scientist. The biggest issue I see is information overload—there’s so much content that you end up learning randomly without a clear “big picture” of what the role actually requires. What helped me was thinking in terms of T-shaped learning: build broad knowledge across the field (Python, SQL, stats, ML, data engineering concepts), then go deep in one or two areas based on the job you want. I also talk about the tutorial trap, because watching video after video can feel productive—until you try to build something and your brain freezes. My framework is “learn to teach”: learn the concept, stop the video, write code, build a mini project, and explain it (even to a friend or in a blog post). Then I cover a mistake I’m personally guilty of: skipping fundamentals to jump into the “cool stuff.” I’ve learned the hard way that you have to understand the “why,” not just the “how,” or you’ll hit a wall when things break. Finally, I explain why interview prep is a separate skill and share my content–practice–simulate cycle to get ready for real interviews under time pressure.