Probability Theory Mastring

This course is a logical continuation of the courses Probability Theory Basics and Learning Statistics with Python courses which cover in more detail some of the topics necessary to learn data science.

It discusses in more detail the concept of a random variable and its characteristics, limit theorems of probability theory, statistical approaches to determining the parameters of a random process, and methods for testing statistical hypotheses.

You will understand two fundamental concepts of probability theory:

• The law of large numbers;
• The central limit theorem.

These concepts are essential in data science to ensure reliable conclusions and accurate predictions. They help handle uncertainty and variability, allowing you to make informed decisions based on data.

Law of Large Numbers

Central Limit Theorem