What are Data Structures? A data structure is a way to store data.

We structure data in different ways depending on what data we have, and what we want to do with it.

Family tree First, let's consider an example without computers in mind, just to get the idea.

If we want to store data about people we are related to, we use a family tree as the data structure. We choose a family tree as the data structure because we have information about people we are related to and how they are related, and we want an overview so that we can easily find a specific family member, several generations back.

• Data structures give us the possibility to manage large amounts of data efficiently for uses such as large databases and internet indexing services.

Data structures are essential ingredients in creating fast and powerful algorithms. They help in managing and organizing data, reduce complexity, and increase efficiency.

In Computer Science there are two different kinds of data structures.

Primitive Data Structures are basic data structures provided by programming languages to represent single values, such as integers, floating-point numbers, characters, and booleans.

Abstract Data Structures are higher-level data structures that are built using primitive data types and provide more complex and specialized operations. Some common examples of abstract data structures include arrays, linked lists, stacks, queues, trees, and graphs.

What are Algorithms? An algorithm is a set of step-by-step instructions to solve a given problem or achieve a specific goal.

Pommes Frites Recipe Pommes Frites Recipe A cooking recipe written on a piece of paper is an example of an algorithm, where the goal is to make a certain dinner. The steps needed to make a specific dinner are described exactly.

When we talk about algorithms in Computer Science, the step-by-step instructions are written in a programming language, and instead of food ingredients, an algorithm uses data structures.

Algorithms are fundamental to computer programming as they provide step-by-step instructions for executing tasks. An efficient algorithm can help us to find the solution we are looking for, and to transform a slow program into a faster one.

By studying algorithms, developers can write better programs.

Algorithm examples:

Finding the fastest route in a GPS navigation system Navigating an airplane or a car (cruise control) Finding what users search for (search engine) Sorting, for example sorting movies by rating The algorithms we will look at in this tutorial are designed to solve specific problems, and are often made to work on specific data structures. For example, the 'Bubble Sort' algorithm is designed to sort values, and is made to work on arrays.

Data Structures together with Algorithms Data structures and algorithms (DSA) go hand in hand. A data structure is not worth much if you cannot search through it or manipulate it efficiently using algorithms, and the algorithms in this tutorial are not worth much without a data structure to work on.

DSA is about finding efficient ways to store and retrieve data, to perform operations on data, and to solve specific problems.

By understanding DSA, you can:

Decide which data structure or algorithm is best for a given situation. Make programs that run faster or use less memory. Understand how to approach complex problems and solve them in a systematic way. Where is Data Structures and Algorithms Needed? Data Structures and Algorithms (DSA) are used in virtually every software system, from operating systems to web applications:

For managing large amounts of data, such as in a social network or a search engine. For scheduling tasks, to decide which task a computer should do first. For planning routes, like in a GPS system to find the shortest path from A to B. For optimizing processes, such as arranging tasks so they can be completed as quickly as possible. For solving complex problems: From finding the best way to pack a truck to making a computer 'learn' from data.

DSA is fundamental in nearly every part of the software world:

Operating Systems Database Systems Web Applications Machine Learning Video Games Cryptographic Systems Data Analysis Search Engines Theory and Terminology

Types of DS :

Array Data Structure

String Data Structure

Linked List Data Structure

Double Linked List Data Structure

Circular Linked List Data Structure

Stack Data Structure

Queue Data Structure

Heap Data Structure

Hash Data Structure

Matrix/Grid Data Structure

Graph Data Structure

Tree Data Structure

Types of Algorithms Here are different type of algorithms which we are going to learn in this tutorial:

DSA - Searching Algorithms

DSA - Sorting Algorithms

DSA - Approximation Algorithms

DSA - Divide and Conquer Algorithms

DSA - Greedy Algorithms

DSA - Recursion Algorithm

DSA - Backtracking Algorithm

DSA - Randomized Algorithms

DSA - Dynamic Programming

DSA - Pattern Searching

DSA - Mathematical Algorithms

DSA - Geometric Algorithms

DSA - Bitwise Algorithms

DSA - Branch and Bound Algorithm

Characteristics of a Data Structure Correctness − Data structure implementation should implement its interface correctly.

Time Complexity − Running time or the execution time of operations of data structure must be as small as possible.

Space Complexity − Memory usage of a data structure operation should be as little as possible.