Numerical Methods and Scientific Computing

Numerical methods are essential for solving complex mathematical equations that cannot be resolved through exact analytical formulas alone. This course provides a structured introduction to numerical analysis, equipping you with the foundational concepts and computational techniques required to approximate mathematical solutions reliably. What you will learn: Understand core mathematical terminology, floating-point representations, and error analysis. Solve non-linear equations and systems using iterative numerical techniques. Apply numerical differentiation and integration techniques to approximate mathematical functions. Solve ordinary differential equations using standard computational algorithms. Implement algorithms efficiently using scientific computing tools like NumPy and SciPy. Starting with fundamental definitions and error bounds, the written lessons progress step-by-step from linear algebra solutions to calculus approximations and algorithm implementations. Designed for beginners and students, this course requires no advanced prior experience in numerical engineering. Start reading today to build a strong practical foundation in computational mathematics.