In this video, I break down exactly how you’re going to become a data scientist in 2026—and if you’re serious about your career, you need to watch it till the end. The biggest mistake I see is people starting with Python. I don’t recommend that. I recommend you start with statistics and machine learning first, because those are the core fundamentals of any data scientist role. Python (or any language) is just a tool to apply data science—it’s not data science by itself. After you build that foundation, I walk you through the “how”: you apply stats and ML through coding, and the number one language you should learn first is SQL because that’s how you source your data. Then you move to Python as your bread and butter—aim for intermediate to advanced skills—and I call out the libraries you should master (NumPy, SciPy, scikit-learn, pandas, statsmodels, plus visualization tools like matplotlib and seaborn). From there, you need to learn the actual data work: EDA, data cleaning, and feature engineering. And one of the most important skills in the age of AI is strong business + product sense and domain knowledge, because that’s what differentiates a good data scientist from a great one. Finally, I’m very clear: skills alone won’t get you hired—you need a project portfolio aligned to real job descriptions, and you need interview prep because data scientist interviews are brutal.