Introduction to Bayesian data analysis

Open to
Government analysts
Training category
Type of training
6 hours
Analysis Function Capability Team
Analysis Function Capability Team


This short course is designed to introduce learners Bayesian analysis and how it can be applied within both the R and Python programming languages.

This course assumes that you have basic statistical and mathematical knowledge. No prior knowledge of Bayesian analysis is required.

This course also assumes basic R and Python knowledge as demonstrated in the Introduction to R and Introduction to Python courses. This includes reading in data and data manipulation using tidyverse (R) and pandas (python).

Additional packages will need to be installed for these courses; it is assumed you will know how to install additional packages, including setting up your computer to do so if required.

Learning outcomes

  • what Bayesian data analysis is
  • examples of Bayesian Analysis
  • a refresher on Probability
  • Bayes’ Theorem
  • the Monty Hall Problem
  • components of Bayesian Analysis
  • approximate Bayesian Computation
  • quadratic Approximation
  • Markov Chain Monte Carlo (MCMC)
  • summarising Posterior Distributions
  • Bayesian Linear Regression Models

How to book

Please use your Learning Hub account to access the course online. Alternatively, please email