Numerical Programming with Python

Bridging the gap between mathematical theory and functional code is a critical skill for modern science and engineering. This course focuses on the logic and structure required to translate numerical analysis concepts into reliable Python programs, moving beyond theoretical derivations to practical execution. You will learn to build your own mathematical solvers from the ground up, ensuring you understand the mechanics behind the algorithms while adopting modern coding standards for scientific computing. What you'll learn: - Implement root-finding algorithms for high-degree equations and non-linear functions - Apply interpolation and curve-fitting techniques to model data sets accurately - Perform numerical differentiation and integration using discrete data and functional inputs - Solve systems of linear equations and ordinary differential equations (ODEs) through iterative methods - Utilize NumPy for efficient array manipulation and high-performance vectorized computations - Apply modern Python type hints and clean code practices to ensure mathematical precision - Understand the transition from custom-built scripts to industry-standard library functions in SciPy The course begins with foundational definitions and key mathematical terminology before moving into step-by-step logic for each numerical method. You will read through detailed explanations of algorithmic structures and analyze code snippets that demonstrate how to handle complex calculations efficiently. This course is designed for beginners who want to learn how to program mathematical solutions; no prior experience with Python or numerical analysis is required. Start building your own library of numerical tools through structured written guidance.