Statistics for data science
Statistics basics covers the summaries. This course covers inference: reasoning from a sample to a decision, running experiments that answer the question, and the errors that survive peer review in industry.
What you'll learn
10 chapters, then the quiz
- 01Samples and populationsWhat you can claim.
- 02The central limit theoremWhy normality keeps appearing.
- 03Confidence intervalsCommunicating uncertainty.
- 04Hypothesis testingThe machinery and its traps.
- 05Multiple comparisonsTesting until something works.
- 06A/B testingExperiments in production.
- 07Causal inferenceWhen you cannot randomise.
- 08RegressionModelling relationships.
- 09A Bayesian viewA different question.
- 10Reporting resultsSo that decisions improve.
More ai & data tests
See all →- Easy
Understanding AI
What machine learning actually does, what a language model is, and where both fall down.
8 questions7 min - Medium
Data analysis
Averages, spread, correlation and the traps that make numbers say the wrong thing.
8 questions7 min - Medium
Machine learning basics
Training, testing and the workflow behind a model that actually works.
8 questions8 min