Data Structures Tutorial

Data Structures Tutorial Asymptotic Notation Structure and Union Array Data Structure Linked list Data Structure Type of Linked list Advantages and Disadvantages of linked list Queue Data Structure Implementation of Queue Stack Data Structure Implementation of Stack Sorting Insertion sort Quick sort Selection sort Heap sort Merge sort Bucket sort Count sort Radix sort Shell sort Tree Traversal of the binary tree Binary search tree Graph Spanning tree Linear Search Binary Search Hashing Collision Resolution Techniques

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Priority Queue in Data Structure Deque in Data Structure Difference Between Linear And Non Linear Data Structures Queue Operations In Data Structure About Data Structures Data Structures Algorithms Types of Data Structures Big O Notations Introduction to Arrays Introduction to 1D-Arrays Operations on 1D-Arrays Introduction to 2D-Arrays Operations on 2D-Arrays Strings in Data Structures String Operations Application of 2D array Bubble Sort Insertion Sort Sorting Algorithms What is DFS Algorithm What Is Graph Data Structure What is the difference between Tree and Graph What is the difference between DFS and BFS Bucket Sort Dijkstra’s vs Bellman-Ford Algorithm Linear Queue Data Structure in C Stack Using Array Stack Using Linked List Recursion in Fibonacci Stack vs Array What is Skewed Binary Tree Primitive Data Structure in C Dynamic memory allocation of structure in C Application of Stack in Data Structures Binary Tree in Data Structures Heap Data Structure Recursion - Factorial and Fibonacci What is B tree what is B+ tree Huffman tree in Data Structures Insertion Sort vs Bubble Sort Adding one to the number represented an array of digits Bitwise Operators and their Important Tricks Blowfish algorithm Bubble Sort vs Selection Sort Hashing and its Applications Heap Sort vs Merge Sort Insertion Sort vs Selection Sort Merge Conflicts and ways to handle them Difference between Stack and Queue AVL tree in data structure c++ Bubble sort algorithm using Javascript Buffer overflow attack with examples Find out the area between two concentric circles Lowest common ancestor in a binary search tree Number of visible boxes putting one inside another Program to calculate the area of the circumcircle of an equilateral triangle Red-black Tree in Data Structures Strictly binary tree in Data Structures 2-3 Trees and Basic Operations on them Asynchronous advantage actor-critic (A3C) Algorithm Bubble Sort vs Heap Sort Digital Search Tree in Data Structures Minimum Spanning Tree Permutation Sort or Bogo Sort Quick Sort vs Merge Sort Boruvkas algorithm Bubble Sort vs Quick Sort Common Operations on various Data Structures Detect and Remove Loop in a Linked List How to Start Learning DSA Print kth least significant bit number Why is Binary Heap Preferred over BST for Priority Queue Bin Packing Problem Binary Tree Inorder Traversal Burning binary tree Equal Sum What is a Threaded Binary Tree? What is a full Binary Tree? Bubble Sort vs Merge Sort B+ Tree Program in Q language Deletion Operation from A B Tree Deletion Operation of the binary search tree in C++ language Does Overloading Work with Inheritance Balanced Binary Tree Binary tree deletion Binary tree insertion Cocktail Sort Comb Sort FIFO approach Operations of B Tree in C++ Language Recaman’s Sequence Tim Sort Understanding Data Processing Applications of trees in data structures Binary Tree Implementation Using Arrays Convert a Binary Tree into a Binary Search Tree Create a binary search tree Horizontal and Vertical Scaling Invert binary tree LCA of binary tree Linked List Representation of Binary Tree Optimal binary search tree in DSA Serialize and Deserialize a Binary Tree Tree terminology in Data structures Vertical Order Traversal of Binary Tree What is a Height-Balanced Tree in Data Structure Convert binary tree to a doubly linked list Fundamental of Algorithms Introduction and Implementation of Bloom Filter Optimal binary search tree using dynamic programming Right side view of binary tree Symmetric binary tree Trim a binary search tree What is a Sparse Matrix in Data Structure What is a Tree in Terms of a Graph What is the Use of Segment Trees in Data Structure What Should We Learn First Trees or Graphs in Data Structures All About Minimum Cost Spanning Trees in Data Structure Convert Binary Tree into a Threaded Binary Tree Difference between Structured and Object-Oriented Analysis FLEX (Fast Lexical Analyzer Generator) Object-Oriented Analysis and Design Sum of Nodes in a Binary Tree What are the types of Trees in Data Structure What is a 2-3 Tree in Data Structure What is a Spanning Tree in Data Structure What is an AVL Tree in Data Structure Given a Binary Tree, Check if it's balanced

About Data Structures

What exactly are data structures?

A data structure is a type of storage that is used to organise and store data. It is a method of organising data on a computer so that it may be easily accessible and modified.

It's essential to pick the correct data format for your project based on your requirements and project. Let us say, in case you wish to store data sequentially in memory, you may use the Array data structure.

In a nutshell, a data structure is a method of arranging data in a computer so that it may be successfully utilised.

To put it another way, keep these two basic equations in mind:

  • Data Structures are formed by combining related data using permissible data operations.
  • And Programs are formed by combining Algorithms Data Structures.
About Data Structures

Data Structure Types

In general, there are two types of data structures:

  • Linear data structure
  • Non-linear data structure

Let's take a closer look at each type.

Linear data structure

The elements of linear data structures are organised one after the other in a sequential order. They are simple to construct since the pieces are placed in a specific order.

However, due of operational complications, linear data structures may not be the ideal solution when the program's complexity grows.

Types of Linear Data Structure

  • Arrays
  • Stacks
  • Queues
  • Linked List

Non-Linear data structure

Non-linear data structures have elements that are not in any particular order, unlike linear data structures. Instead, they're organised in a hierarchical structure, with each piece related to one or more others.

Non-linear data structures are further classified as graph and tree data structures.

Types of Non-Linear Data Structure

  • Graphs
  • Trees
About Data Structures

Data Structures: Linear vs. Non-linear

Let's look at the main distinctions between linear and non-linear data structures now that we've learned about them.

Linear Data StructuresNon-Linear Data Structures
The data pieces are ordered sequentially, one after the other.The data pieces are organised in a non-sequential fashion (hierarchical manner).
All of the things are on a single layer.The data pieces can be found at several tiers.
It can be completed in a single run. That is, if we begin with the first element, we may traverse all of the elements in a single pass.It takes several runs. That instance, if we begin with the first element, we may not be able to traverse all of the elements in a single pass.
The use of RAM is inefficient.Depending on the situation, various architectures use memory in a variety of efficient ways.
The time complexity grows in direct proportion to the magnitude of the data.The time complexity stays constant.
Example: Arrays, Stack, QueueExample: Tree, Graph, Map

Data Structures:  Static and Dynamic

Static structures, as the name indicates, are composed of fixed, permanent structures and sizes at compile time. The array stores a certain quantity of reserve memory that has been predetermined by the programmer ahead of time. Dynamic structures have non-fixed memory capacity that can decrease or increase based on the programme and its execution needs. Furthermore, the location of the related memory might shift.

Data Structures: Homogeneous and Non-Homogeneous

Homogenous data structures, like element collections in an array, are made up of the same data element type. The data in non-homogeneous structures, such as structures, may not have to be of the same type.

The Relationship Between Data Types and Data Structures

To answer the issue of what is data structure, three essential data kinds must be understood.

Abstract.

The behaviour of abstract data defines it. Graphs, queues, stacks, and sets are all examples of this kind.

Combined (or Compound).

Composite data is made up of primitive data types that have been merged, such as arrays, classes, records, strings, and structs. They might also be made up of other composite kinds.

Primitive.

Primitive data, which includes Boolean, characters, integers, pointers, and fixed- and floating-point numbers, is categorised as fundamental data.

These data types serve as the foundation for data structures. Data types inform the interpreter or computer about how the programmer intends to use the data. Data analysts can also select from a variety of data structure categories. The key is to choose the structure that is most suited to your demands and environment.

Why is Data Structure important?

Understanding the operation of each data structure is aided by knowledge of data structures. Based on it, you may choose the best data structures for your project.

This assists you in writing memory- and time-efficient code.

Data structures provide several benefits to IT-related operations, particularly as applications get more complicated and the amount of existing data grows. Here are some of the reasons why data structures are so important.

  • They allow for faster processing rates. Large volumes of data necessitate quicker processing, and data structures aid in the organisation of data into forms that are simpler to work with and analyse.
  • They make it easier to find info. Data structures arrange information into usable formats that make it easier to conduct the necessary searches.
  • They can be reused. Once a data structure is implemented, it may be utilised everywhere. There is no reason to build a new structure. This feature saves both time and money.
  • They make it simple to deal with various demands. A database is rarely accessed by a single person. Instead, hundreds, if not thousands, of people searching and interacting with a database is usual. Data structures organise information so that users don't have to search every item – they can quickly search only the data they need.

Benefits of Data Structures

A data structure has the following advantages:

  • Efficiency: If the data structure used to implement a certain ADT is chosen correctly, the programme will be exceedingly efficient in terms of time and space.
  • Reusability: The data structure is reusable, which implies that it can be used by numerous client applications.
  • Abstraction: The level of abstraction is provided by the data structure given by an ADT. Because the client cannot see the internal workings of the data structure, it does not need to be concerned with implementation. Only the interface is visible to the client.

Significant Operations

The following are the most frequent operations that may be done on data structures:

  • Searching: In a data structure, we may look for any element.
  • Sorting: The elements of a data structure can be sorted in either ascending or descending order.
  • Insertion: The new element can also be inserted into a data structure.
  • Updating: We may also update the element by replacing it with another element.
  • Deletion: To remove an element from a data structure, we can use the delete operation.

Applications of Data Structures

Data structures have a wide range of uses, including:

Storage of data.

Data structures, such as describing attribute collections and related structures used in database management systems to store records, aid in effective data persistence.

Data Transfer.

TCP/IP packets can be used to exchange organised information specified by data structures between programmes.

Management of Resources and Services

Data structures like linked lists can help fundamental operating system resources and services accomplish tasks like file directory management, memory allocation, and processor scheduling queues.

Scalability.

Data structures are used by big data applications to organise and allot data storage across numerous dispersed storage sites. This function ensures scalability and performance.



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