Course 4 of 5
Chance and Data
This course covers the mathematics of uncertainty. You'll see how a gambling dispute led Pascal and Fermat to put numbers on chance, and how probability grew into the basis of insurance, genetics, and much of computing. You'll then look at statistics, which runs the logic the other way, reasoning from data back to the world, and at the field's current arguments over evidence. The examples come from medicine, polling, testing, and measurement.
Who this is for
You can work with variables, solve a linear equation, and work with fractions and percentages. You read news about studies and polls and want to judge them yourself.
What you will learn
- Calculate simple probabilities and expected values, and explain the law of large numbers
- Explain how probability supports insurance, genetics, and randomized computing
- Describe how statistics estimates a population from a sample and states its uncertainty
- Evaluate a statistical claim in a news report, including its sample, design, and measurement
Contents
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Module 1 Probability
This module covers the branch of mathematics that measures chance. You'll see how a 17th-century question about splitting the stakes of an unfinished game started a new field, learn to count outcomes and compute expected values, and see why chance becomes predictable when an experiment is repeated many times. The same ideas price insurance, explain inheritance, and power the random sampling inside today's computers and AI systems.
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Module 2 Statistics
This module covers the branch of mathematics that reasons from data back to the world. You'll see how a 17th-century study of London's death records began the field, how a sample of a thousand people can tell you about millions, and why every estimate comes with a margin of error. You'll then judge the evidence behind medical trials, psychological tests, and published research, including the debate over whether much of it holds up.