Predictive Analytics for Data Science: Linear and Non-Linear Modelling (2-day)
An introduction to linear and non-linear predictive models, providing an interactive step-by-step guide to running these models, and key diagnostics using the R software platform.
Introduction to Mixed Effects Modelling (2-day)
This course is designed as an introduction to mixed effects modelling. These models involve data arising from longitudinal studies or studies where the data exhibits some form of hierarchy, and sometimes referred to as multilevel modelling.
Data Analysis in R
This course will help build participants’ ability to work with data in R and undertake rigorous statistical analysis, including spatial analysis and linear regression, creating publication-standard graphs of the results.
Spatial Analysis in R (2-day)
Designed for the applied users of R, this master-class will show you how to access spatial data from a number of sources, match this with geographic shape files, analyse spatial patterns, link these data to information from surveys, and create interactive maps to highlight important findings.
Applied Longitudinal Data Analysis
This course provides an overview of Longitudinal Data Analysis. As well as the statistical theory, an overview of the many applications and capabilities of LDA is given.
Machine Learning for Data Science: Supervised Learning Techniques (3-day)
An introduction to supervised machine learning techniques for data science, providing an interactive step-by-step guide to running some of the standard statistical regression and classification machine learning models that every data scientist should know.
Foundations of Programming in R for Political Analysis in the Age of AI
This course offers hands-on training in R programming for quantitative political analysis, paired with practical and responsible techniques for using AI assistants to write, debug, and verify data analysis code.
Advanced Statistical Analysis Using R
The focus of this course is on learning advanced statistical methods using R.
Social Network Analysis in R
A practical introduction to the network perspective and social network analysis (SNA), with a focus on applications in political analysis.
Computational Social Science: From Social Networks to Simulating Societies
This is an online, self paced introduction to core concepts, methods and tools in computational social science focusing on three themes: (1) networks as data; (2) text as data; (3) prediction and simulation.
Storytelling with Data Visualisation (3-day)
This master-class has two objectives – to teach the design principles required to make plots that really have an impact on the reader, and to teach the practical skills required to create these plots within the R software package.
Introduction to R (3-day)
This masterclass offers a step-by-step, interactive introduction to R and RStudio for participants with no experience with these software packages.