Amlan

AmlanOct 16, 2026

A stupid mistake I made for years was I tried to learn data the way schools taught me. By topic, in order, completely, before moving on. SQL first, fully. Then Python. Then statistics. Then ML. And so on... It felt rigorous but it was actually wildly inefficient. What works far better is Learn the minimum viable version of a skill and then immediately apply it to a real problem. → Learn enough SQL to pull and join two tables. Then go pull and join two tables on a real dataset. → Learn enough pandas to clean a messy CSV. Then go clean a messy CSV. → Learn enough about LLMs to send a prompt via API. Then go build something tiny. Then circle back to each skill as the problem gets harder. You'll learn 3x faster and you'll have something to show for it at every stage. Complete one topic fully before moving on is a wrong instinct that comes from schools. In school, you're optimizing for tests. In real life, you're optimizing for solving problems. These need very different strategies. 𝐏.𝐒. If you're working full-time and trying to build a data career on the side, I'm here to help: www.datacareerschool.com

AmlanOct 19, 2026

Can we stop treating Data Science like its the natural promotion from Data Analytics? I see so many people learning SQL and Power BI And immediately thinking they need to move into machine learning. But these are different career paths, not necessarily different levels. A good Data Analyst might spend their time understanding business performance and influencing decisions. A Data Scientist might focus more on predictive models and experimentation. Neither is automatically more valuable. So before spending six months learning machine learning Ask yourself whether you actually want to do that kind of work. Or whether you just like the job title.

AmlanOct 18, 2026

You need to work at a MAANG company to have a meaningful career. Complete BS. Some of the strongest data careers I know were built at mid-sized companies most people have never heard of. Let me tell you exactly why: → At a smaller company, one analyst owns entire domains. At MAANG, ten analysts own a slice of one product. → Smaller companies force generalist skills in data engineering, analysis, ML, sometimes ops. MAANG lets you specialize which is nice but limits versatility. → Career velocity: At a scaling company, you can go from analyst to team lead in 2 years. At MAANG, that's usually a 5-year timeline. → Real business context: At smaller companies, you sit in the room with the founder. At MAANG, you're 4 layers away from the decision-maker. MAANG has real advantages like prestige signal, comp, brand for future job hunts. Those matter. I'm not dismissing them. But if your only definition of success is a MAANG logo, you're using someone else's yardstick, not your own. Some of the highest-impact data people I've met have never worked at a Big Tech company. Their careers just don't fit the LinkedIn narrative most people are chasing. What's your version of a successful data career look like?