Reason clearly when outcomes are uncertain.
Move from events and conditional probability to random variables, distributions, Bayes, limit theorems, estimation, and simulation.
What you will leave with
You can translate an uncertain business problem into events, assumptions, distributions, and a computation you can check.
What you will be able to do
The curriculum is organized around these 4 practical outcomes.
Model events with conditional probability
Work with random variables and common distributions
Update beliefs with Bayes theorem
Use simulation and limit theorems to check intuition
Course map
Curriculum
Open a module to inspect every section before you start. Your progress follows you through the course.
What P=0.7 Actually Means, Sample Spaces & Events, The Three Axioms, and more.
What Changes When You Know Something, The Multiplication Rule, Tree Diagrams as a Thinking Tool, and more.
The Belief-Updating Formula, Prior, Likelihood, Posterior, The Disease-Test Paradox, and more.
From Events to Numbers, Discrete vs Continuous, PMF, PDF, and CDF, and more.
Choosing the Right Distribution, Bernoulli & Binomial, Yes/No at Scale, Geometric, Time Until First Success, and more.
Two Random Variables at Once, Joint, Marginal & Conditional, Independence, Formal Definition, and more.
The Law of Large Numbers, The Central Limit Theorem, Why n=30 Matters (and Why It's Not Magic), and more.
Maximum Likelihood Estimation, The MLE Recipe, The Coin Example, Monte Carlo Simulation, and more.
Who this course is for
Start with the background you have. The prerequisite notes above tell you exactly what is assumed.
Data scientists strengthening probability intuition
Analysts moving toward experimentation or ML
Candidates preparing for probability interviews
Continue learning
Each course stands on its own. Pick the one closest to the next task you need to handle.
Build statistical intuition before formulas.
Train models the way working data scientists do.
Design experiments that survive real scrutiny.
Start the course
Module 1 introduces the language and example used throughout the rest of the course.
