Welcome to the DSA-With-Java repository! Here, we will explore various data structures and algorithms implemented in Java. This repository serves as a resource to learn and understand different techniques used in solving problems efficiently.

1. Introduction
2. Data Structures
   • Array
   • Linked List
   • Stack
   • Queue
   • Tree
   • Graph
3. Algorithms
   • Searching
   • Sorting
   • Recursion
   • Dynamic Programming
   • Graph Algorithms

In this repository, we will delve into the world of data structures and algorithms using Java. Understanding these concepts is crucial for writing efficient and optimized code to solve complex problems.

Arrays are fundamental data structures that store elements of the same type in contiguous memory locations. We will explore various operations such as insertion, deletion, searching, and sorting on arrays.

Linked lists consist of nodes where each node contains a data field and a reference to the next node in the sequence. We will implement singly linked lists, doubly linked lists, and explore operations like insertion, deletion, and traversal.

A stack is a Last-In-First-Out (LIFO) data structure that supports two main operations: push (insert) and pop (remove). We will learn about stack implementation and its applications.

A queue is a First-In-First-Out (FIFO) data structure where elements are inserted at the rear end and removed from the front end. We will study queue implementation, different types of queues, and their applications.

Trees are hierarchical data structures consisting of nodes connected by edges. We will cover binary trees, binary search trees, traversal algorithms (inorder, preorder, postorder), and various tree-related problems.

Graphs are collections of nodes (vertices) and edges that connect these nodes. We will explore graph representation, traversal algorithms (BFS, DFS), shortest path algorithms (Dijkstra, Bellman-Ford), and minimum spanning tree algorithms (Prim, Kruskal).

Searching algorithms are used to find a particular element or location within a collection of elements. We will discuss linear search, binary search, and their complexities.

Sorting algorithms arrange elements in a specific order (ascending or descending). We will study various sorting techniques such as bubble sort, selection sort, insertion sort, merge sort, quick sort, and their efficiencies.

Recursion is a programming technique where a function calls itself to solve smaller instances of the same problem. We will explore recursive algorithms and their applications in problem-solving.

Dynamic programming is a method for solving complex problems by breaking them down into simpler subproblems. We will study dynamic programming techniques and solve classic problems using memoization and bottom-up approaches.

Graph algorithms deal with solving problems on graphs. We will cover algorithms for traversing graphs, finding shortest paths, detecting cycles, and constructing minimum spanning trees.