**O-notation**, also known as **Big O notation**, is a mathematical notation used in computer science to describe the **asymptotic behavior of algorithms**. It represents the **upper bound or worst-case scenario of an algorithm's time complexity** regarding the input size. In simpler terms, O-notation expresses how an algorithm's runtime or space requirements grow relative to its input size.
>Note
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>The asymptotic behavior of algorithms refers to how their performance changes as the size of their input approaches infinity. In other words, it describes the algorithm's behavior as the input size becomes very large.
## Commonly used functions in O-notation
1. **O(1):** Constant time. The algorithm's runtime remains constant regardless of the input size. Example: Accessing an element in an array by index;

2. **O(log n):** Logarithmic time. The runtime grows logarithmically with the size of the input. Example: Binary search in a sorted array;

3. **O(n):** Linear time. The runtime grows linearly with the size of the input. Example: Iterating through all elements in an array;

4. **O(n log n):** Log-linear time. The runtime grows in proportion to n multiplied by the logarithm of n. Example: Sorting algorithms like Merge Sort and Quick Sort;

5. **O(n^2):** Quadratic time. The runtime grows quadratically with the size of the input. Example: Nested loops iterate through all pairs of elements in an array;

6. **O(2^n):** Exponential time. The runtime grows exponentially with the size of the input. Example: Generating all subsets of a set;

7. **O(n!):** Factorial time. The runtime grows factorial with the size of the input. Example: Generating all permutations of a sequence.


The understanding of data structures and algorithms: this is what sets advanced professionals apart from the rest. In the modern world of growing data, it is very important to be able to process large amounts of data efficiently. 

In this course, you will study the most basic data structures that help us to deal with huge amounts of data as well as implement the most basic and crucial concepts on your own so you’ll get a deeper understanding of how each data structure works and you'll be able to decide what data structure best suited for your task.

When we start learning something new, it is very important to get familiar with the subject of our study first. So let's dive deeper into what we will learn about in this course.

Arrays and lists serve as foundational data structures in programming, with Python employing them extensively. This section provides an overview of these structures, delving into their concepts and highlighting their implementation nuances within the Python programming language.

There are many applied tasks in which it is necessary to operate with large amounts of structured data and simple data structures such as an array or a linked list may no longer be enough for these purposes. In this section, we'll look at the concept of an abstract data type and how different ADTs allow us to efficiently manage large amounts of data.

In the real world, one often has to deal with tasks in which it is necessary to model complex dependencies between various nodes of a system or domain. Graphs can help us with this. In this section, we will learn what a graph is and how to represent it in computer memory efficiently.

Algorithms and Data Structures Overview

Big-O Notation

Algorithms and Data Structures Overview

Big-O Notation