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Course Description
Probability is the study of regularities that emerge in the outcomes of random experiments. In this course, you'll learn about fundamental probability concepts like random variables (starting with the classic coin flip example) and how to calculate mean and variance, probability distributions, and conditional probability. We'll also explore two very important results in probability: the law of large numbers and the central limit theorem. Since probability is at the core of data science and machine learning, these concepts will help you understand and apply models more robustly. Chances are everywhere, and the study of probability will change the way you see the world. Let’s get random!
Prerequisites
Curriculum
Course outline
1
Let's start flipping coins
A coin flip is the classic example of a random experiment. The possible outcomes are heads or tails. This type of experiment, known as a Bernoulli or binomial trial, allows us to study problems with two possible outcomes, like “yes” or “no” and “vote” or “no vote.” This chapter introduces Bernoulli experiments, binomial distributions to model multiple Bernoulli trials, and probability simulations with the scipy library.
- Let’s flip a coin in Python50 XP
- Flipping coins100 XP
- Using binom to flip even more coins100 XP
- Probability mass and distribution functions50 XP
- Predicting the probability of defects100 XP
- Predicting employment status100 XP
- Predicting burglary conviction rate100 XP
- Expected value, mean, and variance50 XP
- Calculating the expected value and variance50 XP
- Calculating the sample mean100 XP
- Checking the result100 XP
- Calculating the mean and variance of a sample100 XP
2
Calculate some probabilities
In this chapter you'll learn to calculate various kinds of probabilities, such as the probability of the intersection of two events and the sum of probabilities of two events, and to simulate those situations. You'll also learn about conditional probability and how to apply Bayes' rule.
3
Important probability distributions
Until now we've been working with binomial distributions, but there are many probability distributions a random variable can take. In this chapter we'll introduce three more that are related to the binomial distribution: the normal, Poisson, and geometric distributions.
4
Probability meets statistics
No that you know how to calculate probabilities and important properties of probability distributions, we'll introduce two important results: the law of large numbers and the central limit theorem. This will expand your understanding on how the sample mean converges to the population mean as more data is available and how the sum of random variables behaves under certain conditions.
We will also explore connections between linear and logistic regressions as applications of probability and statistics in data science.
Foundations of Probability in Python
Course
Complete
