Introduction to Probability (edX)

You will learn not only how to solve challenging technical problems, but also how you can apply those solutions in everyday life.

With examples ranging from medical testing to sports prediction, you will gain a strong foundation for the study of statistical inference, stochastic processes, randomized algorithms, and other subjects where probability is needed.

What you'll learn

- How to think about uncertainty and randomness

- How to make good predictions

- The story approach to understanding random variables

- Common probability distributions used in statistics and data science

- Methods for finding the expected value of a random quantity

- How to use conditional probability to approach complicated problems

Prerequisites

Familiarity with U.S. high school level algebra concepts; Single-variable calculus: familiarity with matrices. derivatives and integrals.

Not all units require Calculus, the underlying concepts can be learned concurrently with a Calculus course or on your own for self-directed learners.

Units 1-3 require no calculus or matrices; Units 4-6 require some calculus, no matrices; Unit 7 requires matrices, no calculus.

Previous probability or statistics background not required.

Syllabus

Unit 0: Introduction, Course Orientation, and FAQ

Unit 1: Probability, Counting, and Story Proofs

Unit 2: Conditional Probability and Bayes' Rule

Unit 3: Discrete Random Variables

Unit 4: Continuous Random Variables

Unit 5: Averages, Law of Large Numbers, and Central Limit Theorem

Unit 6: Joint Distributions and Conditional Expectation

Unit 7: Markov Chains