Mathematics for Data Science and Statistical Analysis

Data science relies heavily on a solid mathematical foundation, yet many learners struggle to bridge the gap between abstract formulas and practical data analysis. This text-based course demystifies the core mathematical principles required to confidently analyze data, build predictive models, and understand machine learning algorithms. You will transition from basic counting concepts to complex probability models through clear, structured written explanations and step-by-step mathematical breakdowns. What you will learn: Understand fundamental counting principles, permutations, and combinations for data analysis; Apply probability theory, including independent events, joint probability, and Bayes' theorem; Analyze one-dimensional and two-dimensional random variables in data contexts; Evaluate discrete probability distributions such as Bernoulli, Binomial, Poisson, and Geometric; Learn modern data science math concepts including basic vector operations and matrix transformations. The course begins with essential terminology and core mathematical definitions before guiding you through probability theory and discrete distributions with practical data-focused scenarios. Designed specifically for beginners, this course requires no advanced mathematical background and starts from the absolute basics. Begin building your mathematical foundation for data science today.