Data Structures and Algorithms in Java

A comprehensive guide to understanding and implementing data structures and algorithms from scratch using Java, complete with algorithm analysis, Big O notation explanations, and LeetCode problem solutions.

📚 Table of Contents

• About
• Getting Started
• Algorithm Analysis
• Big O Notation
• Data Structures
• Algorithms
• LeetCode Solutions

🎯 About

This repository is a complete resource for learning data structures and algorithms in Java. Whether you're preparing for technical interviews, strengthening your computer science fundamentals, or solving coding challenges, this repository provides:

• Theoretical explanations of algorithm analysis and complexity
• Practical implementations of data structures and algorithms from scratch
• Real-world applications through LeetCode problem solutions
• Clear examples with detailed comments and time/space complexity analysis

🚀 Getting Started

Prerequisites

• Java JDK 21
• An IDE (preferably IntelliJ IDEA)
• Basic understanding of Java programming

Clone the Repository

git clone https://github.com/miladsade96/Data-Structures-and-Algorithms-in-Java.git
cd data-structures-algorithms-java

Running Examples

Each implementation includes a main method for testing. To run an example:

1. Open the desired Java package in your IDE.
2. Locate the Main class.
3. Run the main method to see the output.

📊 Algorithm Analysis

Understanding how to analyze algorithms is crucial for writing efficient code. This section covers:

• What is algorithm analysis?
• Time complexity analysis
• Space complexity analysis
• Best, average, and worst-case scenarios

📈 Big O Notation

Big O notation describes the performance or complexity of an algorithm. This section includes:

Common Time Complexities

Notation	Name
O(1)	Constant
O(log n)	Logarithmic
O(n)	Linear
O(n log n)	Linearithmic
O(n²)	Quadratic

Each complexity includes:

• Detailed explanation
• Java examples with code

🗂️ Data Structures

• Linked List (Singly, Doubly)
• Stacks
• Queues
• Trees
• Hash Tables
• Graphs
• Heaps

Each data structure includes:

• Complete implementation from scratch
• Time and space complexity analysis
• Common operations (insert, delete, search, traverse)
• Related LeetCode problems

🔧 Algorithms

• Recursion
• Tree Traversals
• Basic Sorting Algorithms (Bubble, Selection, Insertion)
• Advanced Sorting Algorithms (Merge, Quick)
• Dynamic Programming

💡 LeetCode Solutions

Each data structure and algorithm section includes solutions to related LeetCode problems:

• Problem statement and link
• Approach explanation
• Complete Java solution
• Time and space complexity analysis
• Alternative solutions when applicable