Diving into data science as a computer scientist means going back to the roots—Statistics, Probability, and Stochastic Processes. While I’ve been applying machine learning algorithms, truly mastering them requires understanding the math behind the magic. That’s why I’ve turned to Udemy to strengthen my foundations, diving deep into statistical inference, probability distributions, and stochastic modeling. It’s one thing to call sklearn.fit()—it’s another way to grasp why gradient descent converges or how Bayesian networks make decisions. This journey isn’t just about coding smarter models; it’s about thinking like a data scientist. Every formula learned bridges the gap between black-box implementations and principled problem-solving.
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