Numerical Methods
Numerical methods are algorithms and techniques for solving mathematical problems that are difficult or impossible to solve analytically, using approximations and iterative computations. They are essential in fields like engineering, physics, finance, and data science for tasks such as solving equations, optimizing functions, and simulating systems. These methods provide practical solutions by leveraging computational power to handle complex or large-scale problems.
Developers should learn numerical methods when working on applications involving scientific computing, simulations, or data analysis where exact solutions are unavailable. For example, in machine learning for gradient descent optimization, in engineering for finite element analysis, or in finance for option pricing models. It enables handling real-world problems with precision and efficiency, bridging theory and practical implementation.