[Free] Numerical Methods With Python For Engineering
Learn Numerical Methods with Python: Roots, Linear Algebra, Integration, and Differential Equations – Free Course
What you’ll learn
- Apply key numerical methods to solve engineering and scientific problems, including roots, integration, differentiation, and ODEs.
- Implement and compare numerical algorithms in Python using NumPy, SciPy, and Matplotlib for accuracy and efficiency.
- Evaluate errors and approximations in numerical methods, including truncation, round-off, and error propagation.
- Model and solve practical engineering problems, applying suitable numerical techniques and visualizing results in Python.
Requirements
- Mathematics: Basic knowledge of calculus (derivatives, integrals) and linear algebra (matrices, vectors).
- Programming: Prior experience with any programming language (Python preferred, but not required).
- Tools: A computer with Python installed (Anaconda or Miniconda recommended) and access to Jupyter Notebook.
- Mindset: Curiosity to apply numerical methods to real-world engineering and science problems.
Description
Numerical methods form the backbone of modern engineering and scientific problem-solving, enabling us to tackle problems that cannot be solved analytically. This course, Numerical Methods with Python for Engineering, is designed to give learners a solid foundation in both the theory and practical application of numerical techniques using Python and its scientific libraries.The course begins with an introduction to the scientific Python ecosystem, including Jupyter, NumPy, SciPy, and Matplotlib. Even if learners have prior programming experience, this module ensures they are comfortable working with Python’s core tools for scientific computing.
Next, students explore the fundamental concepts of approximations and errors, gaining an understanding of accuracy, precision, and error propagation in numerical computations. From there, the course progresses to numerical differentiation and integration, where students learn finite difference methods, trapezoidal and Simpson’s rules, adaptive quadrature, and SciPy’s built-in integration routines.The course then covers systems of linear equations, introducing both direct methods (Gaussian elimination, LU decomposition) and iterative methods (Jacobi, Gauss-Seidel). Students also learn root-finding techniques such as bisection, Newton-Raphson, and fixed-point iteration, with practical implementation in Python.
Further modules include curve fitting and interpolation using regression and splines, followed by solving ordinary differential equations (ODEs) with Euler and Runge-Kutta methods, as well as SciPy’s advanced solvers for stiff and non-stiff systems.
By the end of the course, learners will be able to formulate, implement, and analyze numerical solutions to engineering problems using Python, bridging the gap between mathematical theory and computational practice.
Author(s): Dr.A. Gnanaprakasam
