3.1 Predicting with Probability
Harness data to predict what lies ahead.
Dealing with Uncertainty
From Data to Probabilities
Practice From Data to Probabilities
Visualizing Probabilities
Practice Visualizing Probabilities
Probabilities with Conditions
Practice Probabilities with Conditions
Joint Probabilities
Bayes' Theorem
Practice Bayes' Theorem
Accumulating Probability
Comparing Distributions
Practice Comparing Distributions
Course description
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.
Topics covered
- Probability as counting
- Probability Mass Functions (PMF)
- Cumulative Distribution Functions (CDF)
- Conditional Probability
- Joint Probability
- The Law of Total Probability
- Baye’s Theorem
- Normalization
Prerequisites and next steps
Introduction to Probability Data Analysis Fundamentals Fluency in converting between decimals and percentages.
Up next
3.2 Case Study: Topping the Charts with Spotify
This is the Case Study for Predicting with Probability. In it, you will use Spotify data and probability distributions to recommend songs listeners will love and spot the next song to hit the Top 100.
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