Numerical Methods with Python: A Hands-On Guide with 80+ Worked Examples, Complete Solutions, and Python Code for Engineers and Scientists Kindle Edition

**Master numerical methods by doing—not just reading.**This hands-on textbook teaches you to solve real engineering and science problems using numerical algorithms, hand computation, and Python code. Every method is explained with clear intuition first, then a full mathematical derivation, then step-by-step worked examples showing every arithmetic detail, and finally a clean Python implementation you can run immediately.**What's inside:**- 24 chapters covering the complete numerical methods curriculum: root-finding, interpolation, numerical differentiation and integration, linear systems, eigenvalues, and ordinary differential equations- 80+ fully worked examples with every intermediate calculation shown—no steps skipped- Python implementations using NumPy and SciPy that you can type and run immediately- 51 figures and comparison tables that make concepts visual and methods easy to compare- Complete answer key so you can verify every exercise- Midterm and final cumulative review sections with exam-style problems- Method Quick Reference with 5 comparison tables: when to use which solver, error orders, step-size selection, and SciPy commands- Key Concept and Common Pitfall boxes that highlight what matters most and what trips students up**Who this book is for:**This book is designed for engineering, math, physics, and computer science undergraduates taking a numerical methods or numerical analysis course. It also works well for self-learners and practicing engineers who need to understand scientific computing from the ground up. Prerequisites: single-variable calculus and basic Python.**What makes this book different:**Most numerical methods textbooks are either proof-heavy references that skip computation, or code-dump tutorials that skip the math. This book does both: you understand the algorithm by hand before writing a single line of code. The teach-first approach means you always know *why* a method works, *when* it fails, and *how* to verify your answer is correct.**Topics covered:** Taylor series and error analysis, bisection, Newton's method, secant method, polynomial and spline interpolation, least squares curve fitting, trapezoidal and Simpson's rules, Gaussian quadrature, Romberg integration, Gaussian elimination, LU factorization, iterative solvers (Jacobi, Gauss-Seidel, GMRES), eigenvalue methods, Euler and Runge-Kutta ODE solvers, stiff systems and BDF methods, adaptive step-size control, and applied projects including epidemic modeling and projectile simulation. Read more