Calculus Foundations for Data Science

To truly understand how machine learning models learn and optimize, you must understand the mathematical engine that powers them. This text-based course demystifies calculus, translating complex formulas into clear, intuitive concepts essential for data science. You will transition from basic algebraic concepts to analyzing functions, calculating rates of change, and understanding optimization. By reading through structured explanations and practicing with written exercises, you will build the mathematical intuition needed to confidently work with algorithms, loss functions, and gradient-based optimization. What you'll learn: Understand the fundamental concepts of limits and continuity of functions; Apply differentiation techniques, including first and second-order derivatives, to analyze function behavior; Master optimization principles to find the maximum and minimum values of functions; Explore key mathematical theorems, including Rolle's theorem, Lagrange's mean value theorem, and L'Hopital's rule; Connect calculus concepts directly to modern data science applications like gradient descent. The course begins with essential terminology, basic definitions, and foundational concepts of limits before moving into derivatives, theorems, and practical applications. This course is designed for aspiring data scientists, analysts, and programming beginners who want to build a strong mathematical foundation, with no prior calculus experience required. Start reading today to unlock the mathematical core of modern data science.