Foundations of Probability and Random Variables

Probability is the mathematical backbone of modern data science, machine learning, and statistical analysis. Understanding how to quantify uncertainty allows you to make robust, data-driven decisions with confidence. This text-based course guides you from absolute beginner concepts to a solid grasp of random variables and probability distributions. You will learn to: Understand fundamental probability rules, sample spaces, and conditional probability; Explore discrete and continuous random variables and their mathematical properties; Analyze common probability distributions including Binomial, Poisson, and Normal distributions; Apply Bayes' theorem to update probabilities based on new incoming data; Connect foundational probability theory to modern analytical workflows. You will start with essential terminology and core definitions before progressing to joint distributions and expectation, studying clear written explanations and step-by-step mathematical breakdowns. This course is designed for beginners with basic algebra knowledge, requiring no prior background in advanced calculus or statistics. Start reading today to master the mathematical foundations of uncertainty.