If your goal is AI/ML long term, starting with analytics does help—but only if it’s intentional. Analytics teaches you how data is generated, cleaned, and used in real decisions, which many pure-ML beginners struggle with later.

That said, don’t get stuck there. For a non-IT background, a parallel path works best: use SQL/Excel/BI to build data intuition and employability, while steadily investing in Python, statistics, and ML fundamentals.

Early on, tools like Power BI/Tableau are useful for thinking in metrics and storytelling—but Python + statistics matter more for AI/ML. Dashboards help you explain data; models help you learn from it. The key is sequencing, not choosing sides.