1. Learn
    2. /
  2. Courses
    3. /
  3. Foundations of Probability in Python
  • 1

    Let's start flipping coins

    Free

    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.

  • 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.

Initializing

Exercise

Catching salmon example

Every fall the salmon run occurs -- this is the time when salmon swim back upriver from the ocean to spawn. While swimming back to the upper river (usually to the place where they were spawned), the fish may encounter grizzly bears. Some of these bears can eat 18 salmon in 3 hours, and they have a 0.0333 probability of success in their attempts to catch a fish.

Grizzly bears catching salmons

We can model a grizzly bear catching salmon with a geometric distribution.

For the following exercises, the geom object from scipy.stats has already been loaded for your convenience.

Instructions 1/4

undefined XP
  • 1

    Calculate and print the probability that the bear will catch a salmon on its third attempt, after failing two times.

  • 2

    Calculate and print the probability that the bear will get a salmon in less than 5 attempts.

  • 3

    Calculate and print the probability that the bear will get a salmon in less than 21 attempts.

  • 4

    Calculate and print how many attempts the bear has to make to have a 0.9 probability of catching a salmon.