About this courseSkip About this course
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 learnSkip 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
- 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
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