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Data Structures Tutorial

Home Data Science Data Science Tutorials Data Structures Tutorial

Basics

Linked List Advantages

What is Data Structure?

Heap Data Structure

Types of Trees in Data Structure

AVL Tree in Data Structure

B Tree in Data Structure

B+ Tree in Data Structure

DFS Algorithm

BFS Algorithm

Arrays in Data Structure

Graph in Data Structure

Graph Representation

Breadth First Search

Depth Limited Search

Hashing in Data Structure

Searching in Data Structure

Linear Search in Data Structure

Linked List in Data Structure

Doubly linked list in Data Structure

Circular Linked List in Data Structure

Pointers in Data Structure

Types of Graph in Data Structure

Bubble Sort in Data Structure

Quick Sort in Data Structure

Bitonic Sort

Merge Sort in Data Structure

Selection Sort in Data Structure

Insertion Sort in Data Structure

Radix Sort in Data Structure

Stack in Data Structure

Queue in Data Structure

Priority Queue in Data Structure

Asymptotic Analysis

Tree Traversal in Data Structure

Tree Traversal Techniques

Trie Data Structure

Splay Tree in Data Structure

Spanning Tree Algorithm

Sparse Matrix in Data Structure

Radix Sort Algorithm

Counting Sort Algorithm

Skip List Data Structure

Linked List Algorithm

Linked List Types

Inorder Traversal of Binary Tree

Kruskal's Algorithm

Prim's Algorithm

BFS VS DFS

BCNF

Skip List

Hash Table in Data Structure

Data Structure Interview Questions

Data Structures and Algorithms Interview Questions

AVL Tree Deletion

B+ Tree Deletion

Decision Tree Advantages and Disadvantages

Data Architect Skills

Data Architecture Principles

Data Engineer Jobs

Data Engineer Roadmap

Fundamentals of Data Structure

Circular queue in Data Structure

Spanning Tree in Data Structure

Tree traversal types

Deque in Data structure

Shell Sort in Data Structure

Heap sort in data structure

Heap data structure C++

Heap data structure in Java

Binary Search Tree Types

Binary Tree in Data Structure

Binary Tree Types

Binary Search Tree in Data Structure

Binary Search Tree Advantages

Binary Search Tree Properties

Binary Search in Data Structure

Binary Tree Deletion

Sparse Matrix Multiplication

Preorder Traversal of Binary Tree

Postorder traversal

Decision Tree Hyperparameters

PostOrder Traversal without Recursion

AVL Tree Rotation

Avro File Format

Decision Tree Types

Binomial heap

Confluence Jira Integration

Timm Sort

Depth First Search

Data Structure Tutorial

The data structure is the concept of data being stored and organized in the computer memory and the process of retrieving the data from the data structures. It significantly impacts the performance of data processing for various programming use cases. The data structure is the logical representation of data in the memory.

There are several categories available for data structure and implemented in computer programming. The programming languages support the use of several data structure methodologies while designing and development the code. The data structure is the basic component for complex and multiple data processing implementations.

Why do we need to learn Data Structure?

  • We need to learn data structure to apply computer science best practices for developing efficient business solutions using computer programming.
  • The data structure implementations need a good understanding of the relevant concepts and decisions to apply the best fit data structure for the particular requirements.
  • The data structure knowledge is an added advantage for any software professional who deals with architecting, designing, developing, and optimizing applications using computer programming.
  • Data structure helps the programmers to develop effective and reliable solutions.

Applications of Data Structure

  • Data structure applications are implemented in several industry-specific solutions through sand and advanced programming. The data structure is mainly divided into two categories such as primitive and non-primitive types.
  • The primitive type data structure is also known as data types in most programming languages, such as integer, character, and double types. These primitive data structures are useful to store the single-variable data in the run time of the applications.
  • The non-primitive data structure is the advancement of computer science which is used to store and process multiple data in applications.

The popular non-primitive data types are:

  • Arrays
  • Linked list
  • Stack
  • Queue
  • Binary Trees
  • Graphs
  • These data structures are applied while developing and using computer algorithms for software solutions and product development.
  • The data structures are closely associated with the computer algorithms, which are applied through programming like C and object-oriented programming like Java, C++, and Microsoft C# programming.
  • Moreover, the data structure extends with additional features, capabilities, and the way of interacting with the data structure based upon the programming language.

Example

We will discuss Arrays as an Example of data structure, a mostly used data structure through the programming languages. The array is a non-primitive linear data type that holds similar data types and is managed through an array index.

Following is a conceptual view of an array data structure containing the age of students enrolled for a course.

Data Structure Tutorial

The above example of the array shown contains an integer variable for a student's age in an array declared as the ‘age_Students’ array. It stores 12 students’ age.

The index position will be used to search and retrieve the array element. For instance, to access the 4th element of the array, the data structure will be  age_Students[4] which will return the value of age 23 from the array. The array index starts with  0 or 1 as a base index depending upon the programming languages.

Prerequisites

The basic computer programming techniques and understanding of computer memory concepts.

Target Audience

The target audience for data science tutorials is computer science students, software professionals, product architects, and any IT professional who wants to learn data structure for career enhancement.

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