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⚙️ Java Data Structures & Algorithmic Analysis

Java License: MIT Status: Completed

📘 Project Overview

This repository showcases a comprehensive study of core data structures and algorithmic principles using Java. It blends theoretical foundations with practical implementations, performance analysis, and rigorous testing — designed for academic excellence, technical interviews, and real-world system design.

Each module is structured to demonstrate clarity, scalability, and efficiency, with code written to reflect best practices in object-oriented programming and algorithmic thinking.


🧠 Objectives

  • Implement foundational data structures in Java
  • Analyze time and space complexity using Big O notation
  • Apply generic programming and recursion where appropriate
  • Validate functionality through targeted unit tests
  • Compare performance trade-offs across implementations

📁 Directory Structure

Folder Name Description
Task1_B Java Code Java code examples with annotated complexity analysis
Task 1_C Theoretical breakdown of algorithmic performance using Big O notation
Task2_And Testing various scenarios Stack implementation with getMin() optimization and edge-case testing
Task3 Advanced structures: Linked Lists, Binary Search Trees, Graphs
Task4 Java Collections: HashMap and ArrayList usage with performance comparison

🔍 Key Concepts

Task 1: Algorithm Complexity

  • Big O notation: O(1), O(n), O(log n), O(n²), O(2ⁿ)
  • Time vs Space complexity
  • Code walkthroughs with performance annotations

Task 2: Stack Implementation

  • LIFO principle
  • Dual-stack getMin() design (O(1) time)
  • Generic stack with <T extends Comparable<T>>
  • Unit testing: duplicates, edge cases, empty states

Task 3: Advanced Data Structures

  • Singly & Doubly Linked Lists
  • Binary Search Tree (BST): insert, search, delete, traversal
  • Graph representation via adjacency matrix
  • Applications: LRU cache, auto-suggestion, network mapping

Task 4: Java Collections

  • HashMap: key-value operations, constant-time access
  • ArrayList: indexed storage, filtering with Iterator
  • Time complexity comparison: HashMap vs list-based mapping
  • Use cases: caching, indexing, session tracking

🧪 Testing Strategy

Each implementation is validated through targeted test cases:

  • Functional correctness
  • Edge case handling
  • Console output verification
  • Performance sensitivity

🚀 Getting Started

  1. Clone the repository:
    git clone https://github.com/Mustan-dev/java-data-structures-analysis
    cd java-data-analysis 
    

📖 License

This project is licensed under the MIT License and is intended for educational and portfolio use.


👨‍💻 Author

Mustan
GitHub: github.com/Mustan-dev

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