Course Content

Probability Theory Mastering

## Probability Theory Mastering

1. Additional Statements From The Probability Theory

3. Estimation of Population Parameters

4. Testing of Statistical Hypotheses

# Course Overview

This course is a logical continuation of the Probability Theory Basics and Learning Statistics with Python courses. Its aim is to provide a comprehensive understanding of advanced concepts that can be applied to solve real-life tasks for data analytics and data science positions.

## Course structure

- The first section covers necessary additional topics not addressed in the Probability Theory Basics course. Here, you will gain an understanding of
**random variables**, their**probability distributions**, and the**characteristics**used to describe them. Additionally, you will learn about specific features of the Gaussian distribution, making it widely popular and used; - The second section delves into the
**limit theorems**of probability theory. These theorems form the basis for statistical inference and hypothesis testing, widely employed in solving real-life tasks; - In the third section, we will explore how to
**accurately estimate unknown parameters**of probability distributions from a statistical perspective. Correct estimation is crucial to ensure that our results accurately depict real-life processes we aim to model and analyze; - The final section focuses on
**testing statistical hypotheses**. You will learn about hypotheses, their role in data analysis, and how to test them correctly to obtain informative results.

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