Introduction to Probability: Learn probability, an essential language, and a set of tools for understanding data, randomness, and uncertainty.
About this course on Introduction to Probability
Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you the tools needed to understand data, science, philosophy, engineering, economics, and finance. 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 will learn from Introduction to Probability?
- 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
- 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 on Introduction to Probability
Unit 1: Probability, Counting, and Story Proofs
1.2 Interactive: Birthday Problem
1.3 Practice Problems
Unit 2: Conditional Probability and Bayes’ Rule
2.2 Interactive: Monty Hall Simulation
2.3 Practice Problems
Unit 3: Discrete Random Variables
3.2 Interactive: Normalization
3.3 Practice Problems
Unit 4: Continuous Random Variables
4.2 Interactive: PDF/CDF
4.3 Practice Problems
Unit 5: Averages, Law of Large Numbers, and Central Limit Theorem
5.2 Interactives: Bus Stop Paradox and Central Limit Theorem
5.3 Practice Problems
Unit 6: Joint Distributions and Conditional Expectation
6.2 Interactives: Bivariate Normal, Patterns in Sequences, Bayesian Updating
6.3 Practice Problems
Unit 7: Markov Chains
7.2 Interactive: Markov Chain Simulation
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- 1-3 Months
- Online Course
- Free Course (Affordable Certificate)
- Data Analysis Statistics
- Harvard University