This article is a second of a series and I will cover the parts of probability that are related to data science. Probability is very important for the data scientist and I will try to answer the following questions:

· Basic Concepts of Probability,

· Probability Distributions.

You may find the first article of this series here.

**Types of Probability**

Once again, let us start with the Wikipedia definition for probability: “**Probability** is the branch of mathematics concerning numerical descriptions of how likely an event is to occur, or how likely it is that a proposition is true. The probability of an event is a number between 0 and 1, where, roughly speaking, 0 indicates impossibility of the event and 1 indicates certainty. The higher the probability of an event, the more likely it is that the event will occur.”

**Classical **(or **theoretical**)** probability** is used when each outcome in a sample space is equally likely to occur. The classical probability for an event *E* is given by: