Course
Fundamentals of Bayesian Data Analysis in R
IntermediateSkill Level
Updated 08/2024RProbability & Statistics4 hr23 videos58 Exercises4,450 XP25,797Statement of Accomplishment
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Prerequisites
Introduction to R1
What is Bayesian Data Analysis?
This chapter will introduce you to Bayesian data analysis and give you a feel for how it works.
2
How does Bayesian inference work?
In this chapter we will take a detailed look at the foundations of Bayesian inference.
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.
4
Bayesian inference with Bayes' theorem
Learn what Bayes theorem is all about and how to use it for statistical inference.
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.
Fundamentals of Bayesian Data Analysis in R
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FAQs
Do I need a strong math or statistics background for this Bayesian course?
No. This beginner course only requires Introduction to R. It introduces Bayesian concepts gradually, focusing on building intuition rather than heavy mathematical formulas.
What will I understand about Bayesian analysis after completing this course?
You will understand how Bayesian inference works, why it is useful, how to apply Bayes theorem for statistical inference, and how to analyze continuous data with the Normal distribution.
How does this course compare Bayesian methods to traditional statistics?
A dedicated chapter explains four reasons why Bayesian analysis is a valuable addition to your toolkit, contrasting it with frequentist approaches in practical terms.
Does the course include practical tools for Bayesian analysis in R?
Yes. The final chapter introduces a practical tool for Bayesian analysis in R, allowing you to move beyond theory and start applying Bayesian methods to real data.
How is the course structured across its five chapters?
It progresses from what Bayesian analysis is, through how inference works, why it matters, using Bayes theorem, and finally applying these ideas to more parameters and real data.
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