Data

Probability in Data

Explore how probability underpins data analysis — from basic chance to Monte Carlo simulations. Build the probabilistic thinking skills essential for data science.

1

Basic Probability

Foundations of chance and uncertainty

Lesson 1: Sample Spaces & Events Coming Soon
Lesson 2: Probability Rules Coming Soon
Lesson 3: Complementary Events Coming Soon
2

Conditional Probability

Probability given new information

Lesson 4: Conditional Probability Formula Coming Soon
Lesson 5: Independence Coming Soon
Lesson 6: Multiplication Rule Coming Soon
3

Bayes' Theorem

Updating beliefs with evidence

Lesson 7: Bayes' Formula Coming Soon
Lesson 8: Prior & Posterior Probabilities Coming Soon
Lesson 9: Applications of Bayes Coming Soon
4

Probability Distributions

Patterns of random outcomes

Lesson 10: Discrete Distributions Coming Soon
Lesson 11: Continuous Distributions Coming Soon
Lesson 12: The Uniform Distribution Coming Soon
5

Expected Value

The long-run average outcome

Lesson 13: Calculating Expected Value Coming Soon
Lesson 14: Expected Value Applications Coming Soon
Lesson 15: Linearity of Expectation Coming Soon
6

Variance & Std Dev

Measuring spread and uncertainty

Lesson 16: Variance Coming Soon
Lesson 17: Standard Deviation Coming Soon
Lesson 18: Chebyshev's Inequality Coming Soon
7

Normal Distribution

The bell curve in data

Lesson 19: Properties of the Normal Curve Coming Soon
Lesson 20: Z-Scores & Standardization Coming Soon
Lesson 21: The Central Limit Theorem Coming Soon
8

Sampling

Drawing conclusions from subsets

Lesson 22: Random Sampling Coming Soon
Lesson 23: Sampling Distributions Coming Soon
Lesson 24: Margin of Error Coming Soon
9

Monte Carlo Methods

Simulation-based probability

Lesson 25: What is Monte Carlo? Coming Soon
Lesson 26: Random Number Generation Coming Soon
Lesson 27: Estimating Probabilities by Simulation Coming Soon