Tools 1.7Lesson outlineLesson 7 of 7 in Module 1
Random Numbers and Reproducibility
Generate random data you can reproduce.
Free sample. Everything in this module opens with a free account.Video in production.
Lesson outline: Random Numbers and Reproducibility
In this lesson12 parts
- 01Where this module's data came from, and why it must regenerate
- 02A call that returns different numbers every run
- 03default r n g with a seed: a generator with its own state
- 04random, integers, normal and choice, and the size argument
- 05The generator advances: the same call twice gives different values
- 06Sampling rows with choice, with and without replacement
- 07permutation: one order applied to both arrays
- 08One extra call shifts every value below it
- 09np dot random dot seed, and the legacy global stream
- 10A seed makes a result repeatable, not correct
- 11Regenerating this module's data from its seed
- 12Recap and your challenge
Key terms: Random Numbers and Reproducibility
The words this lesson introduces, each in one line. The module’s glossary collects them all, and the AI glossary has every term across the courses.
- Generator
- Object with its own random state and sampling methods
- Seed
- Starting value used to reproduce a generator's sequence
- Permutation
- Reordering of positions without duplication
Quiz: Random Numbers and Reproducibility
Your first pick on each question is the one that counts. Getting one wrong here is how the lesson sticks.