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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