Course description
Introduction to Probability
Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you 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.
Upcoming start dates
Who should attend?
>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.
Training content
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
Course delivery details
This course is offered through The Georgia Institute of Technology, a partner institute of EdX.
5-10 hours per week
Costs
- Verified Track -$139
- Audit Track - Free
Certification / Credits
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
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