Statistics & Probability for Data Science

The statistics and probability a data scientist actually uses — shown, not just told. Statistics & Probability for Data Science is a visual, example-driven guide to the mathematical foundations behind modern data work, built for people who want intuition first and rigour right behind it.

Starting from statistical thinking and exploratory data analysis, you'll build up probability from the ground: discrete and continuous distributions, the normal distribution and the Central Limit Theorem, estimation and confidence intervals, hypothesis testing, statistical power and effect sizes, and Bayesian statistics. The final chapters connect it all to practice — the Math behind machine learning and advanced topics in statistical data science — and the book closes with practice exercises and a mathematical reference.

You will learn to: - Reason about data, uncertainty, and inference like a statistician - Work confidently with probability distributions - Apply the Central Limit Theorem, confidence intervals, and hypothesis tests - Understand statistical power, effect sizes, and p-values correctly - Use Bayesian thinking in real analyses - See the statistical machinery underneath machine learning

Whether you're moving into data science or shoring up your foundations, this visual guide makes the Math click.