Complement your statistical skills with expert methods in R
The course aims to give students confidence in a high-level professional statistics package in order to use advanced methods that complement the statistical techniques taught on our other modules. Students begin with essential programming skills and progress towards computer-intensive statistical methods such as simulation, bootstrap , optimisation and machine learning methods. Led by Dr Jason Oke, senior statistician in the NDPCHS statistics group, an experienced teaching team guide students from the basics to advanced topics in R.
This course is delivered and assessed wholly online over an intensive 8 weeks.
The last date for receipt of complete applications is 5pm Friday 5th April 2024. Regrettably, late applications cannot be accepted.
The overall aims of this module are to enable students:
- To gain confidence in one high-level professional statistics package, and complement the techniques learnt on other modules with advanced techniques such as bootstrap resampling
Intended learning outcomes are:
- Learn fundamental programming techniques such as loops, resampling, Monte-Carlo simulation and use them to solve analytical problems in medical sciences.
- To develop the ability to complement the techniques learnt on other modules with computer-intensive machine learning type techniques such as random forests and neural networks.
Students should leave the course with confidence that in the future they could manage challenging analytical problems with state-of-the-art R packages; use simulation to evaluate statistical power, or check model assumptions; use bootstrap and permutation methods to calculate confidence intervals and p-values in non-standard situations and utilise machine learning methods to classification and prediction problems.