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Probability - The Science of Uncertainty and Data

Advanced 160 hours English MicrocredentialFREE
SkillsProbabilityRandom variablesDistributionsBayesian inferenceStochastic processesStatistical reasoning

Is this course right for you?

Our take
Worth it if you want a deep, rigorous foundation in probability and you are comfortable with calculus. It is the online version of MIT's long-running probability class, free to audit.

Good for: mathematically comfortable learners who want a deep foundation in probability.

Skip if: you want an applied, light-touch course, or you are not comfortable with calculus.

Over sixteen weeks John Tsitsiklis builds probabilistic modelling from axioms through random variables, Bayesian inference, the laws of large numbers and the Central Limit Theorem, to random processes like Markov chains. It is the theoretical bedrock under serious statistics and machine learning.

It is genuinely advanced and time-consuming — it expects college-level single- and multivariable calculus — so it is the wrong fit for a light, applied course; take an applied statistics course instead if that is you. Free to audit, with an optional paid MITx certificate, and MIT keeps it updated.

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About this course

Probability - The Science of Uncertainty and Data is the online version of MIT's legendary probability class, refined over 50+ years. Across sixteen rigorous weeks it builds probabilistic modelling from the ground up: probability models and axioms, conditioning and independence, discrete and continuous random variables, Bayesian inference, the laws of large numbers and the Central Limit Theorem, and random processes (Bernoulli, Poisson, Markov chains).

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Instructor

JT
John Tsitsiklis
edX instructor

Taught by John Tsitsiklis, Professor of Electrical Engineering and Computer Science at MIT, a member of the National Academy of Engineering who has taught probability for over 15 years.

Frequently asked questions

Hard, and honest about it. This is not a typical light MOOC — it is taught at the rigour of an on-campus MIT graduate-level course, with genuine mathematical depth rather than intuition alone. Each run is semester-length and expects at least ten hours a week. Motivated learners with the right maths do very well, but nobody should mistake it for a gentle introduction.

Solid single and multivariable calculus is essential — sequences, limits, infinite series, the chain rule, and ordinary and multiple integrals all show up. You do not need any prior probability or statistics, which the course builds from scratch, but without comfortable calculus the derivations will quickly become impassable rather than merely challenging.

Yes. It is one of the core courses in MIT's Statistics and Data Science MicroMasters on edX. You can take it as a standalone course, but if you complete the full MicroMasters, credential-holders can apply for accelerated master's pathways at partner universities — though the MicroMasters itself is a professional certificate, not a degree or any guarantee of MIT admission.

The foundations of probability done properly: discrete and continuous random variables, expectations, conditional distributions, and the reasoning behind inference from data. Taught by Professor John Tsitsiklis, it is the probability grounding that serious statistics and machine learning quietly rely on, which is exactly why it sits at the core of a data-science MicroMasters.

You can audit it free on edX to access the lectures; the graded track and the certificate that counts toward the MicroMasters need payment. For anyone heading seriously into data science or wanting genuine probabilistic fluency, the depth is worth it — provided you have the calculus and the time. As a casual overview, it would be far more than you need.
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