A guided technical learning path
Probability & Statistics
Build statistical intuition through simulation and visualization. Understand distributions, hypothesis testing, and Bayesian reasoning—grounded in real data.
Your Learning Path
Each module builds on the last. Open any published lesson or lab and continue at your own pace.
Built for
Who this course helps
- Learners who meet the course prerequisites
- Independent developers building practical depth
- Teams creating a shared technical vocabulary
What you leave with
A practical body of work from Probability & Statistics
- Understand probability fundamentals: events, independence, conditional probability
- Master Bayes' Theorem and apply it to real-world problems
- Work with common distributions: Normal, Binomial, Poisson
Move from explanation to worked examples and practice in one coherent learning path.
About This Course
Probability and statistics are everywhere—from A/B testing to medical trials to machine learning. This course builds your intuition by letting you see the math in action.
Instead of memorizing formulas, you'll generate data, run simulations, and watch distributions emerge. The AI tutor can create synthetic datasets on the fly, helping you understand why the Central Limit Theorem works or why Bayes' Theorem feels counterintuitive.
Inspired by Statistics 110 (Harvard) and Seeing Theory (Brown University).
Prerequisites
- Basic algebra and arithmetic
- Familiarity with Python helpful but not required
What You Will Learn
- Understand probability fundamentals: events, independence, conditional probability
- Master Bayes' Theorem and apply it to real-world problems
- Work with common distributions: Normal, Binomial, Poisson
- Perform hypothesis testing and interpret p-values correctly
- Develop intuition through simulation and visualization
Help shape what we build next
Tell us what you want to learn. This records your interest; it does not enroll you or promise a launch email.