---
title: Data Structures and Algorithms Tutorial - Scaler Topics
description: Learn data structures and algorithms easily from beginner to advanced level, in this guide by Scaler Topics. Know more about the applications of data structures and algorithms along with example programs.
category: Data Structures
---
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Have you ever wondered why most of the product-based companies focus so much on data structures and algorithms in their interviews for positions like Software Development Engineer, Data Scientist, Machine Learning Engineer, and so on?  
If yes, all the answers to your questions lie here.  

It’s always crucial to understand why to learn data structures and algorithms, their needs and applications, and other questions related to data structures and algorithms. Let's deep dive into it.  

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## What is Data Structure?
Data Structure is a way to organize and store data in a computer so that it can be accessed and used efficiently. Data structures provide logical and organized way of representing and manipulating data. 

As an example consider a phonebook where we can search for a person's phone name by looking up their phone number. 
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## Classification of Data Structure?
Data Structures can be classified in two categories:
- Primitive data structure: It can store simple values such as integers, characters, and floating-point numbers.
- Non-primitive data structure: It can store multiple data elements of different types. They are furthur divided in two categories:
    - Linear:  In linear data structures, the data elements are arranged in a sequential manner, such as arrays, linked lists, and stacks.
    - Non-Linear: In non-linear data structures data elements are not arranged in a sequential manner, such as trees and graphs.

Each data structure has its own advantages and disadvantages, and choosing the right one for a specific application is essential for efficient processing and storage of data. 
Consider the above example of PhoneBook. It can be implemented using Hash-tables where each entry consists of a name and phone number.
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## Why Learn Data Structures and Algorithms?
Data Structure is something that can be used to store and organize data in a particular fashion. And, now comes the algorithm. An algorithm is a step-by-step set of instructions to solve a particular problem.  

In simple words, you can say that Data Structures are nothing but “meaningful” arrangements of data that algorithms can use to solve any particular problem!  

Can you imagine going to a library and finding all 10,000 books stored randomly? No! It will be a very hectic task to find the book you want. So we need to create an better or optimized solution to store and search the books instead of a simple solution of randomly searching. And that’s why we need to learn data structures and algorithms and understand their tradeoffs for different situations to be able to create optimized solutions  

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## Need of Data Structures and Algorithms
* **To solve some real-world complex problems:** Yes you heard it right. Consider the above example of searching books in the library, there you can't search books randomly, you need a proper approach to search books in order to save time, and here data structures and algorithms came into play to solve some real-life based problems.  

* **Optimization and Scalability:** Once you have knowledge of data structures and algorithms, you can easily decide which data structure can be used at which place and which algorithm will be best for your use case. This helps in writing more optimized and scalable code.  

* **Improving your problem solving skills:** DSA is your toolbox for tackling some of the toughest challenges in the tech world. From your WhatsApp chat to LinkedIn Feed - everything uses DSA user the hood in some form. Whether you build your own projects, participate in competitive coding contests or work as a software developer - the knowledge of DSA is always helpful.  

* **For job opportunities:** Another point is that these days most product-based companies ask DSA and algorithms in their interviews as they want to judge the problem-solving skills of the candidate. So learning DSA and algorithms will give you an advantage during the interviews and hence can land you in your dream company.  

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## Importance of Data Structures and Algorithms
* **Reducing time complexity**- DSA plays a major role in reducing the time complexity of the code. A problem can be solved using various approaches, but you have to pick the optimized one in order to be more productive and solve the problem in lesser time. It can be done through learning data structures and algorithms.  

* **The core of computer science** - Data structures and algorithms are considered to be the foundation of computer science. With advancements in technologies, more and more data is getting stored. A huge amount of data can slow down the processing speed of computer systems. This is where data structures can help us. They can improve the processing power of the computer by the effective utilization and storage of data.  

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## How to learn data structures and algorithms?
* **Learn DSA from Scaler:** You can learn DSA through the Scaler Topics. We offer a complete series of in depth DSA tutorials along with suitable real life examples. These are targeted for absolute beginners who want to dive into the field of data structures and algorithms.  

* **Learn through books:** You can learn DSA through various available books also. A few famous books are: “Introduction to Algorithms” by Thomas H. Cormen, “The Algorithm Design Manual” by Steven S. Skiena,  “Algorithms” by Robert Sedgewick, and Kevin Wayne, and many more.  

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## Applications of Data Structures and Algorithms

There are a lot of real-life applications of Data structures and algorithms you can see around you. Like Facebook, how that connection and friends logic is built. All that logic is built through Graph data structure.  

Similarly, Google maps uses the Graph data structure internally. So there are tons of applications of data structures and algorithms which you can see all around you.  

Some of the problems that can be solved using DSA and Algorithms are-  

1) Knapsack problem
2) Tower of Hanoi
3) Shortest distance between two points
4) Project scheduling  

and many more...  
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## Audience
The target audience of this tutorials are Computer Science graduates as well as Software Professional who wants to learn data structures and algorithm programming in simple and easy manner.
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## Prerequisites
Prerequisites for these tutorials are:
- You should have basic understanding of any one of below languages:
    - C/C++
    - Java
    - Python
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Hope you get an idea about Data structures and algorithms and their importance and need. One should learn DSA in order to enhance their problem-solving skills and for better job opportunities in good companies.

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Data Structures are used to organise and store data to use it in an effective way when performing data operations. Learn in-depth about the need & applications of data structures, along with complexity analysis, sorting and searching algorithms. This Data Structures Tutorial will guide you to learn Data Structures easily from beginner to advanced level. Also learn implementations of data structures in Python, C, C++ and Java.

Data Structures Tutorial

What is Data Structure?

What Are Data Structures and Algorithms?

Why Learn Data Structures and Algorithms?

Introduction of Data Structure and algorithm

Time Complexity Analysis in Data Structure

Master Theorem (With Examples)

Space Complexity in Data Structure

Complexity analysis of Data Structure and Algorithms

Backtracking Algorithm in Data Structures

Recursion & Backtracking

Recursion Tree Method

Divide and Conquer Algorithm

Binary Exponentiation

Common Problem Solving Techniques

Array Data Structure

Linear Search Algorithm

Binary Search Algorithm

Interpolation Search

Searching Algorithms

What is Sorting?

Stable Sorting Algorithm

Selection Sort Algorithm

Bubble Sort Algorithm

Insertion Sort Algorithm

Quick Sort Algorithm

Heap Sort

Merge Sort Algorithm

Radix Sort Algorithm in Data Structure

Bucket Sort Algorithm

Counting Sort Algorithm

Sorting Algorithms

Median and Order Statistics

MO’s Algorithm - Square Root Decomposition

Array Advanced Algorithms

String in Data Structure

String Data Structure

KMP Algorithm

Z Algorithm

String Pattern Matching

Manacher's Algorithm

String Advanced Algorithms

Singly Linked List

Doubly Linked List

Circular Linked List in Data Structure

Linked List

Stack in Data Structure

Queue in Data Structure

Stacks and Queues

Hashing in Data Structure

Separate Chaining

Open Addressing | Closed Hashing

Double Hashing

Load Factor and Rehashing

Hashing

Tree Data Structure

Binary Tree in Data Structure

Binary Search Tree (BST)

Trie Data Structure

Red Black Tree in Data Structure

Segment Tree

Segment Tree with Lazy Propagation

Persistent Segment Tree

Fenwick Tree | Binary Indexed Trees

AVL Trees

Suffix Trees

Heap Data Structure

Priority Queue in Data Structure

Circular Queue in Data Structure

Treaps

B Tree in Data Structure

B+ Tree

Splay Tree

Sparse Matrix

Trees

Greedy Algorithm in Data Structure

Dynamic Programming

1-D Dynamic Programming Problem

2D DP Problems

State Space Reduction in Dynamic Programming

Digit DP | Data Structures

Bit Masking

Greedy Algorithms and Dynamic Programming

Graph in Data Structure

Types of Graph in Data Structure with Examples

Breadth First Search (BFS) Algorithm

Depth First Search (DFS) Algorithm

Disjoint Set (Union Find Algorithm)

Topological Sorting

Graphs

Biconnected Components

Strongly Connected Components

Articulation Points and Bridges

Graph Connected Components

Spanning Tree

Kruskal's Algorithm

Prim's Algorithm

0-1 BFS

Dijkstra's Algorithm

Bellman–Ford Algorithm

Floyd Warshall Algorithm

Johnson’s Algorithm

Shortest Path Algorithms

Ford-Fulkerson Algorithm for Maximum Flow Problem

Karger's Algorithm for Minimum Cut

Maximum Flow and Minimum Cut

Graph flow algorithms

Hamiltonian Path

Shortest Path in Directed Acyclic Graph

Graph Advanced Algorithms

Sieve of Eratosthenes in Data Structure

Extended Sieve

Primality Test

Euler's Totient Function | Euler's Theorem

Line Sweep Algorithm

Euclidean Algorithm | Basic and Extended

Combinatorics in Data Structure

Principle of Inclusion and Exclusion

Advanced Nim’s Game

Nim Game

Maths and Algebra

Data Structures Tutorial

What to expect from the course