Date: July 2018
This course is designed for those who wish to carry out deeper analyses of their data using statistical models and machine learning methods. Participants should have a grounding in R. The course covers classical modelling (Linear Models, GLMs, Multivariate Analysis), modern statistical methods (Text Mining, Nonlinear Modelling, Bayesian Analysis), and machine learning (both individual methods and combining groups of methods). The course includes new developments in R: specialist mapping tools and Simple Features for spatial analysis, the tidyverse, and integrated and reproducible research using rmarkdown.
R offers a wide selection of modelling tools through its package system. If a new model is introduced in the research literature, it has very likely already been published in R. New (and old) packages need to be checked and evaluated and this is an important aspect of using R that runs through the whole course.
Participants are encouraged to bring their own datasets to work on in the hands-on sessions, which will be tailored to reflect their interests.
R for Analytics
Day 1: Statistical Methods with R
Day 2: Advanced Modelling with R
Maps and Spatial Analysis
R Modelling Case Studies