Modern Statistical Computation and Simulation

Description

Statistical computing skills are a "must-have" for becoming a statistician and are extremely valuable for any quantitative role where you need to undertake data analysis. This course assumes no prior knowledge of computer programming. The intention is to develop the key skills you need and best prepare you for all our statistics courses and wider education in quantitative computing. Following successful completion you will have the skills to develop your own computer program, from scratch, for analysing your data.

In addition to developing your skills and experience in computing, you will also learn about a range of modern statistical techniques which employ the power of computers to analyse complex real world data.

An integral feature of the course is that the lectures and labs are all taught in computer labs so you can instantly get hands-on experience in implementing these statistical techniques efficiently in R, to take advantage of the ample computing power available to your generation.

As well as the fundamental of programming (data structure, logic and control flow, functions, etc.), the course will cover simulation of random numbers which can be used to mimic and thus study real world phenomena. Such simulation tools provide exploratory and inferential techniques to manipulate, visualise and make decisions from complex real world data. In particular, the course will cover:
1) random number generators;
2) simulation studies;
3) permutation and resampling methods (in particular bootstrapping)
4) kernel density estimation
Demonstrations and descriptions of these powerful tools are available on Wikipedia if you want to find out more.