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Data analysis is built on a foundation of mathematical certainty, yet many aspiring analysts struggle with the underlying probability and statistics. This comprehensive, text-based course bridges that gap, taking you from basic counting principles to complex distributions without requiring a prior math degree. You will learn how to interpret data accurately and make decisions based on mathematical proof rather than guesswork. What you'll learn: - Understand fundamental counting principles and basic probability concepts, including union, intersection, and independent events - Apply Bayes' theorem to update probabilities based on new evidence or data - Analyze one-dimensional and two-dimensional random variables in real-world contexts - Differentiate and work with key discrete distributions, including Bernoulli, Binomial, Poisson, and Uniform - Practice formulating hypotheses and interpreting statistical significance for data-driven decisions This course begins with core terminology and foundational definitions before guiding you step-by-step through probability theory, random variables, and discrete distributions. Each section features written explanations and practical data analysis scenarios to reinforce your understanding. This course is designed for absolute beginners, aspiring data analysts, and students looking for a clear, structured introduction to statistics with no advanced mathematical prerequisites. Start building your analytical foundation today.