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 B Tree in Data Structure Convert Sorted List to Binary Search Tree Flattening a Linked List Given a Perfect Binary Tree, Reverse Alternate Levels Left View of Binary Tree What are Forest Trees in Data Structure Compare Balanced Binary Tree and Complete Binary Tree Diameter of a Binary Tree Given a Binary Tree Check the Zig Zag Traversal Given a Binary Tree Print the Shortest Path Given a Binary Tree Return All Root To Leaf Paths Given a Binary Tree Swap Nodes at K Height Given a Binary Tree Find Its Minimum Depth Given a Binary Tree Print the Pre Order Traversal in Recursive Given a Generate all Structurally Unique Binary Search Trees Perfect Binary Tree Threaded Binary Trees

What Should We Learn First? Trees or Graphs in Data Structures

A data structure is a database used to store and manage data and optimize and manage computing resources. A data structure is a form used intelligently and quickly to store, process, organize, and store data in a machine or computer format. Data structures help represent data for ease of use and manipulation.

Data structures are not just for storing data but also for processing, storage, and preservation. Almost every program or application contains basic and advanced data structures. We have many simple and advanced data structures, and finding a program in a programming language that does not have a data structure is complicated. Storage used to organize and store data is known as the data structure. It is a method of managing computerized data to translate or retrieve it more efficiently.

Every basic program, software, or application consists of two components: data and algorithm - data communication and algorithmic rules and regulations. We can depict all the above in the following two simple equations:

Operations that are permissible on the data + Related data = Data Structure

Algorithms + Data structures = Programs

There are two types of data structures

  • Linear Data structures
  • Non-linear data structures

Linear Data structures

In linear data structures, each element is related and followed by a component. So you can delete the instance. There are four types of this data structure. They are:

  • Linked lists
  • Queue
  • Array
  • Stack

Non-linear Data structures

In Non-linear data structures, content is not a series of information presented at different levels. There are several ways to pass from one element to another indirectly. Unauthorized data points are shared at least once. There are two types of this non-linear data structure. They are:

Tree Data structure

The Tree is a document editor and is a great way to organize your content and quickly transfer it to your computer. A tree data structure consists of child, structure, and root nodes associated with fields, and this data also contains leaves, branches, and related roots.

Tree data is organized into columns, groups, arrays, and linked lists. A tree is a hierarchical structure; A data model consists of nodes representing and supporting processes. Asynchronous data stores store different data types in a tree-like data structure called a root. All kinds of data are stored in a central location. Each line contains a message. The branches at the bottom of the Tree are called data models.

A tree is a hierarchical, non-linear file with nodes. Each tree section contains a table of values and store names that refer to other units ("Children").

Trees are considered non-linear because they are hierarchical. The data in the Tree is non-linear. But it is organized differently, more precisely, hierarchically.

A type of binary Tree. This is for security purposes only and is a private message. Since this data structure is a separate tree, it can have at most two children. A binary tree can always have 0, 1, or 2 children. A binary search tree can traverse a nested list and match its speed. This allows a binary tree to provide the benefits of a linked list and an array, making it easier to find hidden objects (since they are mutable data structures).

Advantages of trees in data structures

  1. Data is represented in the hierarchical format in trees
  2. As the tree data structures are dynamic, the count of nodes forming the tree data structure is not limited.
  3. As the tree data structures are dynamic, the count of nodes forming the tree data structure is not limited.
  4. It takes an average time to perform any insertion and deletion operations in trees.
  5. Keys can be organized as a tree (like in a BST (Binary search tree)). This helps search any given tree easier and quicker than Linked Lists. However, it is slower compared to arrays. Red-black trees and AVL (Adelson, Velskii & Landis Tree) are self-balancing search trees that guarantee an upper bound of O(log N) for search.
  6. The implementation of pointers in the tree data structure is similar to linked lists but differs from arrays. This means that the nodes are linked using pointers and do not have an upper limit.
  7. The tree data structure is used as a Decision tree.

Graph Data structure

The graph has two sets, considered V and E. These vertices are also called nodes, and edges are referred to as arcs connecting any two nodes in a graph. In a simple sentence, we can say that graph- is a set of vertices (V) and edges (E) which is denoted as G (V, E). These are usually accustomed to solving several real-time problems efficiently.

These represent networks like a city's telephones, circuitry, and paths. These are also used on many social platforms like Facebook, LinkedIn, etc. Let us consider an example of each user or person on Facebook as a vertex (or node) in the graph.

Each vertex has information about the user, like name, gender, etc., and the connection between each node or vertex is considered an edge or arc. These are also used in the operating system as resource location graphs.

These are also used in Google maps to find the shortest distance between two places, these are also used in airlines to know the best effective route, used in transportation, in circuits it is used to represent the nodes or circuitry points, these are used to solve challenging puzzles like mazes, these are also used in computer networking, there are many advantages of using graphs like it is straightforward to work with the algorithms like DFS and BFS, it has many practical applications, because of its nature(non-linear data structure) it is used in solving many complex problems, it helps in understanding and visualizing complex issues. As we know, every coin has the other side.

Advantages of graphs

  1. Neighbors of given nodes, the shortest paths between nodes, and many more can be easily found with the help of graphs.
  2. DFS (Depth first search) and BFS (Breadth-first search) algorithms are implemented using graphs.
  3. Minimum spanning trees are identified with the help of graphs. These trees have many applications.
  4. Data is organized with the help of graphs.
  5. As graphs are non-linear data structures, they help in problem visualization and understanding complex problems.
  6. In computers, their flow of control is represented using graphs.
  7. Social networking sites like Facebook, Instagram, etc., use graphs. On these websites, the users are treated as nodes, and the relations between them are treated as edges.
  8. Resource allocation graphs are used in operating systems.
  9. Google maps use graphs. The shortest route on the map is found in google maps using graphs.
  10. Graphs are used in state transition diagrams. Their respective states and transitions are represented using graphs.
  11. Graphs are also used in algorithms that help find the shortest transportation path.
  12. Graphs are used in algorithms which include circuits. In these cases, the wires are represented by the edges, and the nodes represent the circuit points.
  13. Graphs are also used in algorithms used in games which include solving puzzles or mazes with one solution.
  14. Computer networks that have peer-to-peer (P2P) applications use graphs.
  15. Graphs are also used in blockchain that is used for cryptocurrency, and directed acyclic graphs (DAG) are used for these purposes. Nano, IOTA, and other cryptos mainly based on Directed acyclic graphs (DAG) can be examples of graph algorithms used in blockchains for cryptocurrency.

What should we learn first: Trees or Graphs in data structures?

If we notice, both trees and graphs have many similar terminology and properties. Both trees and graphs have nodes and edges. In both of them, data is stored in nodes whose relations are connected via edges. But trees are hierarchical, unlike graphs. We can only have one path between any two chosen nodes in trees. But this is not the case in graphs. We can have any number of paths between two selected nodes in graphs. Hence, we can conclude that trees are a subset of graphs.

Therefore, it would be easier to excel in trees if we learned about graphs first. Hence, teaching graphs before trees is recommended while learning data structures in any programming language.