Theory of Probability

Course Description

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. 

Course Details

  • Intensive Study: Data Science
  • Online Format: Synchronous - This course is taught in real-time, and students are expected to attend virtual sessions at specific times during the week. For more information on the schedule options for this course, please visit the Stanford Explore Courses website.


Math 52 and familiarity with Infinite Series, or equivalent.

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