Introduction to Bayesian data analysis

Open to
Government analysts
Training category
Analytical
Type of training
Online
Length
6 hours
Organiser
Analysis Function Capability Team
Provider
Analysis Function Capability Team
Location
Online

Description

This short course is designed to introduce learners to 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

During this course you will learn about:

  • 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 Data.Science.Campus.Faculty@ons.gov.uk.

Contact

If you would like more information about this course, please email Data.Science.Campus.Faculty@ons.gov.uk.

Related courses

Introduction to R

Introduction to Python