Probability - The Science of Uncertainty and Data

  • Free to audit
  • Paid certificate
  • 16 weeks
Provider
MIT
Course code
6.431x
Cost
Free to audit
Certificate
Paid, $300
Duration
16 weeks
Format
Instructor-paced
Language
English
Subjects
Mathematics, Data Science
Source
MIT Open Learning
Last verified
17 Sep 2026

Free access to the material ends after the course length; graded work and the certificate are paid.

6.431x is MITx's probability course and part of the MicroMasters Program in Statistics and Data Science. It is based on MIT's long-running Introduction to Probability class and covers all the basic probability concepts: multiple discrete and continuous random variables, expectations and conditional distributions, laws of large numbers, the main tools of Bayesian inference, and an introduction to random processes including Poisson processes and Markov chains. The material is developed intuitively but with mathematical precision, rather than in a theorem-proof format, and the emphasis is on concepts and methods that apply universally even though the examples are wide-ranging. MIT describes it as a challenging class that prepares you to apply probability to real-world problems or research.

The course runs instructor-paced over an estimated 16 weeks. Auditing is free and includes access to the course and materials during the session; MIT's help centre says audit learners lose access shortly after the session ends. The certificate track costs $300 and provides graded assignments and exams plus an MIT certificate on completion, which can count toward the MicroMasters credential alongside the programme's other courses. The classroom counterpart, 6.041SC, is on OpenCourseWare without grading.

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What you’ll learn

  • Describe the basic structure and elements of probabilistic models
  • Work with random variables, their distributions, means and variances
  • Carry out probabilistic calculations
  • Apply Bayesian inference methods
  • Use laws of large numbers and their applications
  • Model random processes such as Poisson processes and Markov chains

Who it’s for

Students and professionals with calculus who want a rigorous foundation in probability before statistics or machine learning.

Instructors

  • Karene Chu MITx Digital Learning Scientist, Statistics & Data Science MicroMasters Program, MIT
  • John Tsitsiklis MIT
  • Patrick Jaillet MIT
  • Dimitri Bertsekas MIT

Course details

Prerequisites
College-level calculus (single-variable & multivariable). Comfort with mathematical reasoning; and familiarity with sequences, limits, infinite series, the chain rule, and ordinary or multiple integrals.

Source: MIT Open Learning (opens in a new tab) · Verified · Report a change

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