{"product_id":"probability-and-statistics-essentials-for-data-science-and-machine-learning-200-examples-and-figures","title":"Probability and Statistics Essentials for Data Science and Machine Learning: 200+ examples and figures","description":"\u003cdiv\u003e \u003cdiv\u003e \u003cdiv\u003e \u003cp\u003e\u003cspan\u003eUpdated 2026 Edition\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eMost probability and statistics books stop short of the algorithms a working data scientist actually runs. Most machine learning books skip the math underneath.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eThis book is the bridge.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003eIt builds probability and statistics from the ground up, then carries the same thread into the algorithms data scientists use every day, including Maximum Likelihood Estimation, Linear Regression, and Logistic Regression.\u003c\/span\u003e\u003c\/p\u003e\n\u003cp\u003e\u003cspan\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan\u003eWhat makes it different:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eAnchored in Real Business Problems:\u003c\/span\u003e\u003cspan\u003e The book runs on real business problems (say, a LinkedIn marketing team optimizing engagement, or a product-page A\/B test deciding whether a new layout is actually better). Concepts are introduced exactly when the problem needs them, not in abstract topic order, and every problem ends with a concrete business answer.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eBuilt From First Principles:\u003c\/span\u003e\u003cspan\u003e Every concept is reasoned out from the ground up. The book first explains why a concept exists, what problem it solves, and where it fits, then introduces the notation. The reader follows the logic, not just the symbols.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eThe Unbroken Thread:\u003c\/span\u003e\u003cspan\u003e The book is built so each chapter compounds on the last. By the time you reach the machine learning chapters, you have already worked through the probability and statistics they run on.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eA Visual-First Approach:\u003c\/span\u003e\u003cspan\u003e The book makes heavy use of figures. Concepts are designed to be seen, not just stated.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan\u003eInside the book:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eProbability foundations: sample spaces, axioms, conditional probability, Bayes' theorem\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eRandom variables and distributions: Bernoulli, Binomial, Poisson, Exponential, Log-normal, Beta, and the Normal distribution\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eJoint, marginal, and conditional distributions, and the Law of Large Numbers\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eDescriptive statistics: central tendency, dispersion, association, and visualization\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eInferential statistics: sampling distributions, the Central Limit Theorem, confidence intervals, hypothesis testing, p-values, Type I and Type II errors, and statistical power\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eMaximum Likelihood Estimation, with closed-form and numerical optimization, applied to Logistic and Normal models\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eSimple and Multiple Linear Regression, derived via Ordinary Least Squares, with full model assessment (RMSE, R²) and per-coefficient significance testing using the hypothesis testing framework from earlier chapters\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eLogistic Regression, derived end-to-end from Maximum Likelihood Estimation (sigmoid, likelihood, Newton-Raphson), with assessment via confusion matrix, accuracy, precision, recall, specificity, ROC, and F1\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan\u003eWho it is for:\u003c\/span\u003e\u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eStudents who want to break into data science or machine learning.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eData scientists and ML engineers who learned statistics by absorption and want to fill the gaps.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eSoftware engineers transitioning into data science or machine learning.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eCandidates preparing for data science and quant interviews.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003cli\u003e\u003cspan\u003e\u003cspan\u003eAnalysts who want statistical depth behind the methods they already use.\u003c\/span\u003e\u003c\/span\u003e\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003cspan\u003e\u003cbr\u003e\u003c\/span\u003e\u003cspan\u003eFinish this book, and the gap between probability, statistics, and machine learning closes for good.\u003c\/span\u003e\u003c\/p\u003e \u003c\/div\u003e \u003c\/div\u003e \u003c\/div\u003e","brand":"Books Prime","offers":[{"title":"Default Title","offer_id":45925946065087,"sku":"0445d468-95ba-4dd1-8459-97f2634c5bd7","price":48.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0761\/7550\/7647\/files\/74bfb38e84ed35c529dfc799fd2c6f3f.jpg?v=1785261100","url":"https:\/\/books-prime.com\/products\/probability-and-statistics-essentials-for-data-science-and-machine-learning-200-examples-and-figures","provider":"Books Prime","version":"1.0","type":"link"}