Probability and Statistics for Data Science

Data science relies heavily on a strong mathematical foundation, yet many aspiring analysts struggle to bridge the gap between theoretical formulas and practical application. This course simplifies complex mathematical concepts, transforming abstract equations into intuitive, actionable knowledge. By learning the core principles of probability and statistics, you will gain the confidence to analyze data patterns, make accurate predictions, and understand the algorithms powering modern data models. What you'll learn: Understand fundamental counting principles and their applications in data analysis; Apply probability rules, including independent events, intersections, and Bayes' theorem; Analyze one-dimensional and two-dimensional random variables; Master discrete distributions such as Bernoulli, Binomial, Poisson, and Geometric; Practice modeling real-world data scenarios using statistical distributions. You will begin by exploring essential mathematical terminology and foundational counting concepts. From there, you will progress through core probability theories, step-by-step distribution models, and practical scenarios that show how these mathematical structures apply to modern data science. This course is designed for beginners who want to build a strong mathematical background for data careers, with no prior advanced math experience required. Take your first step toward mastering the mathematics of data analysis today.