Probability Theory Basics | Description of Track Courses
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?

Section 1. Chapter 4

Course Content

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?

Section 1. Chapter 4