Much in life is left to chance, from next week's weather to the stock market to the traits we pass on to our children.

This course provides hands-on experience extracting predictions about the future from weather and airline data.

By the end, you will know how to work with probability mass functions (PMF), cumulative distribution functions (CDF), joint and conditional probabilities, and Bayes' Theorem.

Use past data to make probabilistic predictions

Visualize probabilities with probability mass functions

Probability Mass Functions

Dealing with Uncertainty

From Data to Probabilities

Visualizing Probabilities

Use conditional probability to find the likelihood outcomes

Apply Bayes' theorem to refine predictions

Conditional Probability

Probabilities with Conditions

Joint Probabilities

Bayes' Theorem

Visualize flight cancellations with cumulative distribution functions (CDFs)

Compare airline reliability with CDF plots

Cumulative Distribution Functions

Accumulating Probability

Comparing Distributions

Apply probability calculations to Spotify song data

Case Study

Spotify's Top 100

Matching a Single Feature

Matching Multiple Features

Give personalized recommendations with conditional probability

Evaluate the performance of recommendation systems

Predicting with Probability

Level 1Probability Mass Functions

Level 2Conditional Probability

Level 3Cumulative Distribution Functions

Level 4Case Study

Level 5Case Study Part 2

Course description

Topics covered

Prerequisites and next steps