The purpose of the course is to introduce students to Monte Carlo methods and their uses. Areas to be covered include: Origins of Monte Carlo methods, uncertainty propagation with Monte Carlo, the Bootstrap method, data fitting, inversion, optimization and Bayesian sampling. 4 lectures over two days followed by a computer practical. The practical will involve hands on use of MC methods using a computer. Solutions to exercises will be provided and previous experience with MATLAB and or python would be advantageous. (Students may use other programming languages to attempt the exercises if they wish.) The course is aimed primarily at first time users of Monte Carlo methods. Those who want to find out what they are and how to use them. Expertise in physics, mathematics and computing will not be assumed, although might be of help.