Data Analysis · Level 3

3.1 Predicting with Probability

Harness data to predict what lies ahead.

8 Lessons5 Practice

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

Data Analysis · Level 3

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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