Calculus and Optimization for Data Science

Data science and machine learning rely heavily on mathematics, yet many practitioners struggle to understand the underlying calculus that powers modern algorithms. This course bridges that gap by breaking down complex mathematical theories into clear, digestible, and practical written concepts.\n\nYou will transition from memorizing formulas to deeply understanding how functions behave, how rates of change are calculated, and how algorithms find optimal solutions. By working through structured explanations and step-by-step written exercises, you will build the mathematical intuition required to write better code and understand advanced data science models.\n\nWhat you'll learn:\n- Understand fundamental calculus terminology, including functions of a single variable, limits, and continuity.\n- Apply differentiability concepts to measure rates of change and analyze function behavior.\n- Calculate maxima and minima to solve single-variable optimization problems.\n- Explore Taylor series and their role in approximating complex functions.\n- Connect calculus principles directly to data science applications, such as gradient descent and loss minimization.\n- Practice solving optimization problems through guided, step-by-step written exercises.\n\nThe course begins with foundational definitions of functions and limits before moving into derivatives, differentiability, and optimization. You will progress from basic calculations to understanding how these concepts are applied to modern machine learning algorithms.\n\nThis course is designed for aspiring data scientists, analysts, and programmers who want to build a strong mathematical foundation. No prior calculus experience is required.\n\nStart reading today to master the essential calculus that drives data science algorithms.