Numerical Methods: Practical Computational Mathematics with Python

Mathematical models are the backbone of modern engineering, data science, and physics, but solving them analytically is often impossible. Computational mathematics bridges this gap by using algorithms to find precise numerical solutions. In this text-based course, you will learn how to translate mathematical theories into working code. You will progress from understanding foundational concepts to writing clean, structured algorithms that solve algebraic, differential, and systems of equations. What you'll learn: - Understand foundational concepts of computational errors, machine precision, and algorithm stability. - Solve non-linear equations using bisection, Newton-Raphson, and secant methods. - Implement numerical integration techniques including the trapezoidal and Simpson's rules. - Apply linear algebra methods to solve systems of linear equations computationally. - Interpolate data points using polynomial and spline interpolation techniques. - Write clean, modern Python code using type hints and NumPy to execute numerical algorithms efficiently. The course begins with core definitions and error analysis before guiding you through structured, text-based laboratory exercises designed to build your practical computational skills. This course is designed for students, developers, and aspiring data scientists who want to build a solid foundation in numerical methods without needing advanced prior programming experience. Start exploring the power of computational mathematics today.