Mathematical Foundations: Derivatives, Probability, and Statistics

Many modern technical fields, from data science to finance, require a firm grasp of underlying mathematical principles. Without a solid understanding of calculus and probability, advanced concepts like machine learning algorithms or risk models remain a black box. This course demystifies these critical mathematical areas, guiding you from foundational definitions to practical applications. By reading through clear, step-by-step breakdowns and solving structured written exercises, you will develop the quantitative intuition needed to tackle complex analytical problems with confidence. What you'll learn: Understand the fundamental principles of derivatives, limits, and rates of change; Apply differentiation rules to solve optimization problems and understand gradient-based algorithms; Master core probability concepts, including conditional probability, Bayes' theorem, and random variables; Analyze data using key statistical measures, probability distributions, and hypothesis testing; Interpret mathematical models used in modern data analysis and quantitative decision-making. The course begins with core terminology and foundational definitions before moving into practical mathematical techniques. You will progress systematically from single-variable calculus into probability theory and statistical inference through clear written explanations and solved practice problems. This course is designed for beginners, aspiring data professionals, and students looking to refresh their quantitative skills, requiring only basic algebra as a starting point. Start building your mathematical foundation today.