Training & Capacity Building

Training & Capacity Building

LIDA offers a range of data analytics programmes for academic and non-academic researchers.  The courses, which take place in our ideally equipped training room, range from introductory courses for postgraduate students through to advanced training for data scientists.

Upcoming training courses

View all upcoming training courses at LIDA.


R Courses

As spatial datasets get larger, more sophisticated software needs to be harnessed for their analysis. R is now a widely used open source software platform for statistical analysis and is increasingly popular for those working with spatial data thanks to its powerful analysis and visualisation packages.

We offer a range of courses including:

  • Introductory R for Spatial Analysis
  • Introduction to Forecasting in R
  • Intermediate and advanced R for spatial data
  • R for Spatial Microsimulation

GIS Courses


We run a range of GIS courses throughout the year:

  • An Introduction to GIS – Using ArcGIS – (Vector)
  • Introduction to Geographical Information Systems – Using ArcGIS (Raster Applications)
  • GIS for Transport Studies (ArcGIS – Vector)

Spatial methods for public health researchers

This one day workshop provides an overview of spatial scales in the UK and different spatial methods which may be applicable to public health research. Geodemographic classifications are used in a working example with open source software.


Computer Programming Summer School

This hands-on seven day summer school aims to bring social scientists without any programming experience to the point where they can program their own social science models and applications. It provides a beginner’s-level introduction to computer programming using examples drawn from social science. It also introduces key libraries, methodologies, and platforms available for social science programmers.


Big Data and Predictive Analytics for Social Science Research

This one day course is aimed at social science PhD students and introduces new concepts in big data and depends on an appreciation of challenges relating to inference, hypothesis generation, statistical analysis, mathematical modelling. Although detailed prior knowledge of one or more of these areas is not essential, a broad knowledge of issues surrounding the use of data in social science research will be assumed.