Numerical Analysis 2

Martin Lotz – School of Mathematics – The University of Manchester


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

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.

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.

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