Janesh Devkota has completed

# Foundations of Probability in R

4 hours
4,350 XP

## Course Description

Probability is the study of making predictions about random phenomena. In this course, you'll learn about the concepts of random variables, distributions, and conditioning, using the example of coin flips. You'll also gain intuition for how to solve probability problems through random simulation. These principles will help you understand statistical inference and can be applied to draw conclusions from data.

### .css-1goj2uy{margin-right:8px;}Group.css-gnv7tt{font-size:20px;font-weight:700;white-space:nowrap;}.css-12nwtlk{box-sizing:border-box;margin:0;min-width:0;color:#05192D;font-size:16px;line-height:1.5;font-size:20px;font-weight:700;white-space:nowrap;}Training 2 or more people?

Try DataCamp for BusinessFor a bespoke solution book a demo.
1. 1

### The binomial distribution

Free

One of the simplest and most common examples of a random phenomenon is a coin flip: an event that is either "yes" or "no" with some probability. Here you'll learn about the binomial distribution, which describes the behavior of a combination of yes/no trials and how to predict and simulate its behavior.

Play Chapter Now
Flipping coins in R
50 xp
Simulating coin flips
100 xp
Simulating draws from a binomial
100 xp
Density and cumulative density
50 xp
Calculating density of a binomial
100 xp
Calculating cumulative density of a binomial
100 xp
Varying the number of trials
100 xp
Expected value and variance
50 xp
Calculating the expected value
100 xp
Calculating the variance
100 xp
2. 2

### Laws of probability

In this chapter you'll learn to combine multiple probabilities, such as the probability two events both happen or that at least one happens, and confirm each with random simulations. You'll also learn some of the properties of adding and multiplying random variables.

3. 3

### Bayesian statistics

Bayesian statistics is a mathematically rigorous method for updating your beliefs based on evidence. In this chapter, you'll learn to apply Bayes' theorem to draw conclusions about whether a coin is fair or biased, and back it up with simulations.

4. 4

### Related distributions

So far we've been talking about the binomial distribution, but this is one of many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial: the normal, the Poisson, and the geometric.

### GroupTraining 2 or more people?

collaborators

prerequisites

Introduction to R
David Robinson

Principal Data Scientist at Heap

Dave is the Principal Data Scientist at Heap. He has worked as a data scientist at DataCamp and Stack Overflow, and received his PhD in Quantitative and Computational Biology from Princeton University. Follow him at @drob on Twitter or on his blog, Variance Explained.
See More