Welcome to CodeShare-DSA! This repository serves as a collaborative learning hub for exploring various data structures and algorithms. Dive into code snippets, detailed explanations, and insightful discussions to enhance your understanding of fundamental concepts in computer science.

Join our community to share your knowledge, collaborate with fellow developers, and embark on a journey of continuous learning and improvement. Let's code, learn, and grow together in the fascinating world of data structures and algorithms!

👩‍💻 Happy Coding! 👨‍💻

1. Introduction
2. Arrays/String
3. Linked Lists
4. Stacks
5. Queues
6. Sorting Algorithms
7. Searching Algorithms
8. Graph Algorithms
9. Dynamic Programming
10. Hashing
11. Tree and Tree Traversal
12. Bit Manipulation
13. Miscellaneous

Problem Solving Techniques

1. Divide and Conquer
2. Two Pointers
3. Sliding Window
4. Binary Search
5. Graph Traversal
6. Greedy Algorithms
7. Dynamic Programming
8. Backtracking
9. Memoization
10. Bit Manipulation

Welcome to CodeShare-DSA!

CodeShare-DSA is a collaborative learning hub for Data Structures and Algorithms (DSA). This repository is dedicated to providing a comprehensive collection of DSA resources, including theory, code snippets, problem-solving techniques, and algorithm implementations.

Whether you're a beginner looking to learn the fundamentals of DSA or an experienced developer seeking to enhance your skills, you'll find valuable resources here to support your journey. From arrays to dynamic programming, this repository covers a wide range of topics essential for mastering DSA.

Feel free to explore the repository, learn from the resources available, and contribute your own knowledge and expertise to help others on their learning path. Together, let's build a vibrant community of DSA enthusiasts and empower each other to become better programmers.

In programming, arrays and strings are fundamental data structures used to store and manipulate collections of values. Arrays are ordered collections of elements, while strings are sequences of characters. Both arrays and strings are commonly used in JavaScript for various purposes.

Arrays in JavaScript are dynamic, meaning they can grow or shrink in size as needed. They can store elements of any data type and support various operations such as insertion, deletion, and traversal.

JavaScript Example:

// Create an array
let arr = [1, 2, 3, 4, 5];

// Accessing elements
console.log(arr[0]); // Output: 1

// Insertion
arr.push(6); // Append 6 to the end of the array

// Deletion
let index = arr.indexOf(3);
if (index !== -1) {
  arr.splice(index, 1); // Remove element 3 from the array
}

// Traversal
arr.forEach(num => console.log(num)); // Output: 1, 2, 4, 5, 6


# Sorting
arr.sort()  # Sort the array in ascending order

# Searching
index = arr.index(4)  # Find the index of element 4

# Reversing
arr.reverse()  # Reverse the order of elements in the array


//String

# Create a string
my_string = "Hello, World!"

# Concatenation
new_string = my_string + " How are you?"

# Slicing
substring = my_string[7:12]  # Extract "World" from the string

//String Manipulation

# Splitting
words = my_string.split(",")  # Split the string into a list of words

# Joining
new_string = "-".join(words)  # Join the words with hyphens

# Formatting
formatted_string = "My name is {} and I am {} years old".format("Alice", 30)

//Advanced Techniques

# Two-pointer approach
left, right = 0, len(arr) - 1
while left < right:
    # Swap elements at left and right pointers
    arr[left], arr[right] = arr[right], arr[left]
    left += 1
    right -= 1

Linked lists are a fundamental data structure consisting of a sequence of elements, each containing a reference to the next element in the sequence. Unlike arrays, linked lists do not have a fixed size and can grow or shrink dynamically. There are various types of linked lists, including singly linked lists, doubly linked lists, and circular linked lists, each with its own advantages and use cases.

Overview:

In a singly linked list, each node contains a data element and a reference (or link) to the next node in the sequence. The last node points to null, indicating the end of the list. Singly linked lists are simple and efficient in terms of memory usage, but they do not support backward traversal.

JavaScript Example:

class Node {
    constructor(data) {
        this.data = data;
        this.next = null;
    }
}

class SinglyLinkedList {
    constructor() {
        this.head = null;
    }

    append(data) {
        const newNode = new Node(data);
        if (!this.head) {
            this.head = newNode;
            return;
        }

        let current = this.head;
        while (current.next) {
            current = current.next;
        }
        current.next = newNode;
    }

    display() {
        let current = this.head;
        while (current) {
            console.log(current.data);
            current = current.next;
        }
    }
}

// Example usage:
const singlyLinkedList = new SinglyLinkedList();
singlyLinkedList.append(1);
singlyLinkedList.append(2);
singlyLinkedList.append(3);
singlyLinkedList.display();

Overview:

In a doubly linked list, each node contains a data element and two references (or links): one to the next node and one to the previous node in the sequence. This allows for both forward and backward traversal of the list. Doubly linked lists require more memory compared to singly linked lists due to the additional reference for backward traversal.

class Node {
    constructor(data) {
        this.data = data;
        this.prev = null;
        this.next = null;
    }
}

class DoublyLinkedList {
    constructor() {
        this.head = null;
    }

    append(data) {
        const newNode = new Node(data);
        if (!this.head) {
            this.head = newNode;
            return;
        }

        let current = this.head;
        while (current.next) {
            current = current.next;
        }
        current.next = newNode;
        newNode.prev = current;
    }

    display() {
        let current = this.head;
        while (current) {
            console.log(current.data);
            current = current.next;
        }
    }
}

// Example usage:
const doublyLinkedList = new DoublyLinkedList();
doublyLinkedList.append(1);
doublyLinkedList.append(2);
doublyLinkedList.append(3);
doublyLinkedList.display();

Overview:

In a circular linked list, the last node points back to the first node in the sequence, forming a circular loop. This means that there is no null reference, and traversal of the list can start from any node. Circular linked lists can be either singly or doubly linked. They are useful in applications where data needs to be continuously processed in a loop.

class Node {
    constructor(data) {
        this.data = data;
        this.next = null;
    }
}

class CircularLinkedList {
    constructor() {
        this.head = null;
    }

    append(data) {
        const newNode = new Node(data);
        if (!this.head) {
            this.head = newNode;
            newNode.next = this.head; // Make the new node point to itself to form a circular loop
            return;
        }

        let current = this.head;
        while (current.next !== this.head) {
            current = current.next;
        }
        current.next = newNode;
        newNode.next = this.head; // Make the new node point back to the head to form a circular loop
    }

    display() {
        let current = this.head;
        do {
            console.log(current.data);
            current = current.next;
        } while (current !== this.head);
    }
}

// Example usage:
const circularLinkedList = new CircularLinkedList();
circularLinkedList.append(1);
circularLinkedList.append(2);
circularLinkedList.append(3);
circularLinkedList.display();

Overview:

A stack is a linear data structure that follows the Last In, First Out (LIFO) principle, where elements are inserted and removed from the same end called the top of the stack. It can be visualized as a collection of elements with two main operations: push (to add an element to the top of the stack) and pop (to remove the top element from the stack).

• There are primarily two types of stacks:
  • Array-based Stack: This type of stack is implemented using arrays. It utilizes the built-in array methods like push() and pop() to perform stack operations efficiently. The array-based stack is simple to implement and suitable for small-sized stacks.
  • Linked List-based Stack: This type of stack is implemented using a linked list data structure. Each node in the linked list represents an element in the stack, and stack operations are performed by manipulating the pointers of the linked list nodes. The linked list-based stack is dynamic in size and can efficiently handle large-sized stacks.

Example: Stack Implementation using Array Explanation:

In this example, we'll implement a stack using an array in JavaScript. The array's built-in methods, such as push() and pop(), make it convenient to implement the stack operations. JavaScript Example:

class Stack {
    constructor() {
        this.items = [];
    }

    push(element) {
        this.items.push(element);
    }

    pop() {
        if (this.isEmpty()) {
            return "Underflow";
        }
        return this.items.pop();
    }

    peek() {
        return this.items[this.items.length - 1];
    }

    isEmpty() {
        return this.items.length === 0;
    }

    display() {
        let result = "";
        for (let i = this.items.length - 1; i >= 0; i--) {
            result += this.items[i] + " ";
        }
        console.log(result.trim());
    }
}

// Example usage:
const stack = new Stack();
stack.push(1);
stack.push(2);
stack.push(3);
stack.display(); // Output: 3 2 1
stack.pop();
stack.display(); // Output: 2 1

[ Details description with example]

[Explore different sorting algorithms like Bubble Sort, Merge Sort, etc.]

[Discuss searching algorithms such as Linear Search, Binary Search, etc.]

[Cover algorithms related to graphs, such as Depth-First Search (DFS), Breadth-First Search (BFS), etc.]

[Explain dynamic programming concepts and techniques.]

[Discuss hashing and its applications in data structures and algorithms.]

[Describe tree data structures and different methods of traversal.]

[Include any additional topics or algorithms not covered in the previous sections.]

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3. Make your changes directly to the README.md file.
4. Ensure your changes are clear, concise, and relevant to the project.
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Once you have made your changes to the README.md file, submit them by following these steps:

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4. Explain the purpose of your changes in the pull request description.
5. Submit the pull request and wait for it to be reviewed.

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• Avoid making unnecessary changes or adding irrelevant content.
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By contributing to this project, you agree to abide by these guidelines and adhere to the project's code of conduct.

We appreciate your contributions to improving our README.md file!

Have questions about data structures, algorithms, or problem-solving techniques? Check out our frequently asked questions below:

1. Question: What is the difference between Greedy and Dynamic Programming?

   • Answer: Greedy algorithms make decisions based on the current best choice without considering the overall solution, while dynamic programming breaks down a problem into smaller sub-problems and stores the results of sub-problems to avoid redundant computations.

2. Question: How do you compare the time complexity of Bubble Sort and Merge Sort?

   • Answer: Bubble Sort has a time complexity of O(n^2), while Merge Sort has a time complexity of O(n log n). This means that Merge Sort is typically faster for large datasets due to its better time complexity.

3. Question: What is the difference between Depth-First Search (DFS) and Breadth-First Search (BFS)?

   • Answer: DFS explores as far as possible along each branch before backtracking, while BFS explores all neighbor nodes at the present depth before moving on to the nodes at the next depth level.

4. Question: When should I use a stack instead of a queue?

   • Answer: Use a stack when you need to access elements in a Last-In-First-Out (LIFO) manner, such as in reversing a string or implementing function call stack. Use a queue when you need to access elements in a First-In-First-Out (FIFO) manner, such as in implementing breadth-first search (BFS).

5. Question: What are the advantages of using a hash table over an array?

   • Answer: Hash tables offer constant-time average case for insertion, deletion, and lookup operations, while arrays require linear time for these operations in the worst-case scenario. Additionally, hash tables can handle dynamic resizing more efficiently than arrays.

This repository is maintained by Dibyank Singh.

Contributions by Dibyank Singh and other amazing contributors.

A heartfelt thank you to all contributors for their valuable contributions to this project!

This repository is licensed under the MIT License 📝.