Computational Mathematics

Python, MATLAB and reliable numerics. Engineering mathematics on a computer: NumPy, SciPy and SymPy for numeric and symbolic work, MATLAB/Octave syntax and toolboxes, vectorisation, floating-point pitfalls and good practice for trustworthy engineering code.

• Python for Engineering Mathematics — Python with NumPy, SymPy, SciPy and Matplotlib is the free standard toolkit for engineering computation. Learn how symbolic (exact) and numerical (floating-point) computing differ, core NumPy arrays and vectorisation, linear algebra, SymPy calculus and ODE solving, floating-point pitfalls, and a clean workflow for engineering scripts.
• MATLAB & Octave for Engineers — MATLAB (and the free, largely compatible GNU Octave) is built around matrices and remains the industry standard in control, signal processing and many labs. Learn the syntax for matrices, element-wise operators, the backslash solver, scripts and functions, plotting, polynomial fitting, numerical integration, ODE solvers and how MATLAB idioms map to Python.