# Probability Theory Basics

**Probability theory** explores randomness and uncertainty in math. It gauges the odds of outcomes in uncertain scenarios. Used in stats, ML, finance, physics, bio, and engineering.

## Why do we need probability theory?

Probability theory is commonly used to solve various real-life tasks:

**Uncertainty Modeling**: models and quantifies uncertainty, aiding decisions and predictions in real-world scenarios;**Statistics and Data Analysis**: underpins stats and data analysis. It offers tools for parameter estimation, hypothesis testing, and drawing data-based conclusions;**Machine Learning**: is vital in ML, including Bayesian methods, graphical models, and reinforcement learning;**Risk Assessment and Decision Making**: in finance and beyond, probability assesses risks, guides investments, and informs decisions amid uncertainty;**Experimental Design**: in scientific experiments, probability theory helps in designing experiments, analyzing data, and drawing reliable conclusions;**Natural Phenomena Modeling**: in physics and engineering, probability models and explains complex natural randomness.

## Why is this course included in the track?

Probability theory is vital for data scientists, aiding in uncertainty reasoning, data-driven choices, robust experiments, ML models, and insightful data interpretation. Proficiency in it is key for effective problem-solving and success.

### Example

Calculate the probability that a randomly chosen man will have a height greater than 180 cm.

Everything was clear?

Course Content

Preparation for Data Science Track Overview

## Preparation for Data Science Track Overview

# Probability Theory Basics

**Probability theory** explores randomness and uncertainty in math. It gauges the odds of outcomes in uncertain scenarios. Used in stats, ML, finance, physics, bio, and engineering.

## Why do we need probability theory?

Probability theory is commonly used to solve various real-life tasks:

**Uncertainty Modeling**: models and quantifies uncertainty, aiding decisions and predictions in real-world scenarios;**Statistics and Data Analysis**: underpins stats and data analysis. It offers tools for parameter estimation, hypothesis testing, and drawing data-based conclusions;**Machine Learning**: is vital in ML, including Bayesian methods, graphical models, and reinforcement learning;**Risk Assessment and Decision Making**: in finance and beyond, probability assesses risks, guides investments, and informs decisions amid uncertainty;**Experimental Design**: in scientific experiments, probability theory helps in designing experiments, analyzing data, and drawing reliable conclusions;**Natural Phenomena Modeling**: in physics and engineering, probability models and explains complex natural randomness.

## Why is this course included in the track?

Probability theory is vital for data scientists, aiding in uncertainty reasoning, data-driven choices, robust experiments, ML models, and insightful data interpretation. Proficiency in it is key for effective problem-solving and success.

### Example

Calculate the probability that a randomly chosen man will have a height greater than 180 cm.

Everything was clear?