15. Big O Notation

Описание: 📌 Video Description

Big O Notation is a fundamental concept in computer science, data science, and algorithm design that helps us understand how the speed and performance of a function or algorithm grow as the number of data elements increases.

In this video, we explain Big O notation as a way to measure the order of growth—how an algorithm’s time or space requirements scale with input size. Rather than focusing on exact execution time, Big O helps compare algorithms based on efficiency and scalability, which is critical when working with large datasets.

This lesson is essential for writing efficient code, choosing the right algorithms, and understanding performance trade-offs in real-world systems.

🎯 What You’ll Learn in This Video

What Big O Notation means

Understanding growth rate and algorithm speed

Why Big O focuses on scalability, not exact time

How performance changes as data size increases

Common Big O examples (O(1), O(n), O(n²), O(log n))

Why Big O matters in data science and software engineering

👨‍💻 Who This Video Is For

Computer science and programming beginners

Data science students

Software developers

Anyone learning algorithms and performance analysis

🔑 Keywords (SEO)

Big O Notation, Time Complexity, Algorithm Efficiency, Growth Rate of Algorithms, Order of Growth, Data Structures and Algorithms, Computer Science Basics, Performance Optimization

👍 Don’t Forget

Like 👍 | Share 🔁 | Subscribe 🔔 for more computer science and data science fundamentals.

📌 Hashtags

#BigONotation #TimeComplexity #Algorithms #ComputerScience #DataScience #ProgrammingBasics #TechFundamentals