Probability Theory is the branch of mathematics that deals with uncertainty, underpinning all of statistics and machine learning. It has applications in specialized fields of data analysis, such as physics, meteorology, web searching, and econometrics.

This means that any good data scientist should have at least an intermediate understanding of probability theory. This is why probability questions tend to come up regularly in data science job interviews.

The main object of study in probability is events. An event is simply an outcome of some experiment, such as flipping a coin.

Experiments are defined by the fact that they are non-deterministic, in that we may do the same experiment twice and get two different outcomes (such as getting heads or tails).

In this first course, we will explore the fundamental notions of probability and why these notions are defined in certain ways.