Computation is an important part of Numerical Analysis, and students are expected to engage with a numerical computing system. Code associated to the lectures and tutorials is available here.
MATLAB is the standard for scientific computing. Two recommended introductions to MATLAB are
- Desmond J. Higham and Nicholas J. Higham, MATLAB Guide, Second edition, SIAM 2005.
- An introduction to MATLAB by Dr. Stefan Güttel.
The first chapter of the Higham and Higham book (A Brief Tutorial) seems to be available through Google Books. The built-in guide (for example, Getting Started in the Help menu) to MATLAB is also useful, and the best way to learn MATLAB is by going through examples.
A useful MATLAB package for numerical computing with functions is Chebfun.
The powerful programming language Python can be used as a free alternative to MATLAB and is very easy to learn. Python is possibly the most popular programming language for all things related to data science.
A recommended introduction to Python is
- Stefan Güttel and Vedran Šego, MATH20622 – Programming with Python
You may also want to install the Anaconda Python distribution.
- Continuum Analytics, Anaconda Python .
Python code can be executed on the CoCalc platform, without the need to install a Python distribution.
Julia is a fairly new high-level programming language for scientific computing. It is similar in style to MATLAB, but is freely available. As with Python, Julia code can be executed on the CoCalc platform.