Univariate Differentiation with Python

Calculus is the mathematical foundation behind modern machine learning algorithms and data science models. Understanding how functions change and how to calculate their derivatives is essential for anyone looking to step into quantitative fields. This text-based course guides you from the absolute basics of single-variable functions to implementing derivatives directly in Python code. You will start by learning foundational mathematical concepts, defining limits, and understanding the geometric meaning of a derivative. Then, you will apply algebraic rules to compute derivatives of polynomial, exponential, and trigonometric functions. Finally, you will translate these mathematical concepts into clean, executable Python code using modern scientific libraries. What you'll learn: - Understand the core concepts of limits, continuity, and the definition of a derivative - Apply fundamental differentiation rules including the product, quotient, and chain rules - Compute derivatives of univariate functions using NumPy and SciPy - Implement numerical differentiation techniques to approximate derivatives in code - Analyze optimization problems by finding critical points and local extrema - Write clean Python scripts using virtual environments to manage your scientific dependencies This course begins with clear, step-by-step written explanations of calculus theory before moving into practical code implementations. You will read structured explanations, study clear code examples, and practice your skills with targeted exercises. This course is designed for beginners, data enthusiasts, and aspiring programmers who want to build a solid mathematical foundation. No prior calculus experience is required, though a basic familiarity with Python variables and functions is helpful. Start reading today to bridge the gap between calculus theory and practical Python programming.