paid course

Foundations of Probability in R

  • 4 hours
  • 13 Videos
  • 54 Exercises
  • 2,157 Participants
  • 4350 XP
David Robinson
David Robinson

Data Scientist, Stack Overflow

Dave is a Data Scientist at Stack Overflow. He received his PhD in Quantitative and Computational Biology from Princeton University and his interests include statistics, data analysis, education, and programming in R. Follow him at @drob on Twitter or on his blog, Variance Explained.

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  • Nick Carchedi

    Nick Carchedi

  • Tom Jeon

    Tom Jeon

  • Nick Solomon

    Nick Solomon


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.

  1. 1

    The binomial distribution


    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.

  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. 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. 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.