# Fundamentals of Bayesian Data Analysis in R

Learn what Bayesian data analysis is, how it works, and why it is a useful tool to have in your data science toolbox.

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## Course Description

Bayesian data analysis is an approach to statistical modeling and machine learning that is becoming more and more popular. It provides a uniform framework to build problem specific models that can be used for both statistical inference and for prediction. This course will introduce you to Bayesian data analysis: What it is, how it works, and why it is a useful tool to have in your data science toolbox.

- 1
### What is Bayesian Data Analysis?

**Free**This chapter will introduce you to Bayesian data analysis and give you a feel for how it works.

A first taste of Bayes50 xpUnknowns and ice creams50 xpLet's try some Bayesian data analysis50 xpCoin flips with prop_model100 xpZombie drugs with prop_model100 xpSamples and posterior summaries50 xpLooking at samples from prop_model100 xpSummarizing the zombie drug experiment100 xpYou've done some Bayesian data analysis!50 xp - 2
### How does Bayesian inference work?

In this chapter we will take a detailed look at the foundations of Bayesian inference.

The parts needed for Bayesian inference50 xpTake a generative model for a spin100 xpTake the binomial distribution for a spin100 xpUsing a generative model50 xpHow many visitors could your site get (1)?100 xpRepresenting uncertainty with priors50 xpAdding a prior to the model100 xpBayesian models and conditioning50 xpUpdate a Bayesian model with data100 xpHow many visitors could your site get (3)?100 xpWhat have we done?50 xp - 3
### Why use Bayesian Data Analysis?

This chapter will show you four reasons why Bayesian data analysis is a useful tool to have in your data science tool belt.

Four good things with Bayes50 xpExplore using the Beta distribution as a prior100 xpPick the prior that best captures the information50 xpChange the model to use an informative prior100 xpContrasts and comparisons50 xpFit the model using another dataset100 xpCalculating the posterior difference100 xpDecision analysis50 xpA small decision analysis 1100 xpA small decision analysis 2100 xpChange anything and everything50 xpThe Poisson distribution100 xpClicks per day instead of clicks per ad100 xpBayes is optimal, kind of...50 xp - 4
### Bayesian inference with Bayes' theorem

Learn what Bayes theorem is all about and how to use it for statistical inference.

Probability rules50 xpCards and the sum rule100 xpCards and the product rule100 xpCalculating likelihoods50 xpFrom rbinom to dbinom100 xpCalculating probabilities with dbinom100 xpBayesian calculation50 xpCalculating a joint distribution100 xpConditioning on the data (again)100 xpA conditional shortcut100 xpBayes' theorem50 xpA Poisson model description50 xp - 5
### More parameters, more data, and more Bayes

Learn about using the Normal distribution to analyze continuous data and try out a tool for practical Bayesian analysis in R.

The temperature in a Normal lake50 xprnorm, dnorm, and the weight of newborns100 xpA Bayesian model of water temperature50 xpA Bayesian model of Zombie IQ100 xpEyeballing the mean IQ of zombies?50 xpAnswering the question: Should I have a beach party?50 xpSampling from the zombie posterior100 xpBut how smart will the next zombie be?100 xpA practical tool: BEST50 xpThe BEST models and zombies on a diet100 xpBEST is robust100 xpWhat have you learned? What did we miss?50 xp

Collaborators

Prerequisites

Introduction to RRasmus Bååth

See MoreData Science Lead at castle.io

Rasmus Bååth is a Data Science Lead at castle.io. Previously, he was an instructor and Curriculum Lead for Projects at DataCamp. He has a PhD in Cognitive Science from Lund University in Sweden. Follow him at @rabaath on Twitter or on his blog, Publishable Stuff.

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