Introduction to Bayesian data analysis

Open to
Government analysts
Training category
Type of training
6 hours
Data Science Campus Faculty
Data Science Campus Faculty

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

Who this course is for

This course assumes that you have basic statistical and mathematical knowledge. You do not need to have knowledge of Bayesian analysis to enrol on this course.

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 in R, and pandas in Python.

You will need to install additional software packages to complete this course. You will need to know how to do this, including how to set up your computer to install these packages.

Learning outcomes

On this course you will learn:

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

You will also see some examples of Bayesian Analysis, and refresh your memory on the subject of probability.

How to book

Please use your Learning Hub account to access the course online. If you do not have a Learning Hub account, please email


If you would like more information about this course, please email

Related courses

Introduction to R

Introduction to Python