Probability - The Science of Uncertainty and Data

Overview

The world is full of uncertainty: accidents, storms, unruly financial markets, noisy communications. The world is also full of data. Probabilistic modeling and the related field of statistical inference are the keys to analyzing data and making scientifically sound predictions.

Probabilistic models use the language of mathematics. But instead of relying on the traditional "theorem-proof" format, we develop the material in an intuitive -- but still rigorous and mathematically-precise -- manner. Furthermore, while the applications are multiple and evident, we emphasize the basic concepts and methodologies that are universally applicable.

This course is part of a MicroMasters® Program

The Probability - The Science of Uncertainty and Data  certificate at Massachusetts Institute of Technology - MITx, part of the MITx MicroMasters Program in Statistics and Data Science covers all of the basic probability concepts, including:

• multiple discrete or continuous random variables, expectations, and conditional distributions

• laws of large numbers

• the main tools of Bayesian inference methods

• an introduction to random processes (Poisson processes and Markov chains)