Transform repository into comprehensive Data Structures and Algorithms tutorial

[Copilot]

This PR transforms the basic repository into a complete educational resource for data structures and algorithms. The repository now serves as a comprehensive tutorial with working implementations, detailed explanations, and practical examples.

What's New

📊 Data Structures (6 implementations)

• Arrays: Dynamic array operations with insertion, deletion, and search
• Linked Lists: Singly linked list with position-based operations
• Stacks: LIFO operations with practical balanced parentheses checker
• Queues: Both regular and circular queue implementations
• Binary Search Trees: Complete BST with all traversal methods
• Hash Tables: Hash map with collision handling via separate chaining

🚀 Algorithms (13 implementations)

Sorting: Bubble, Selection, Insertion, Merge, Quick, and Heap sort with performance comparisons

Searching: Linear, Binary, Jump, Interpolation, Exponential, Ternary, and Fibonacci search algorithms

📚 Educational Features

Each implementation includes:

• Time/Space Complexity Analysis with Big O notation
• Detailed documentation explaining when and why to use each structure/algorithm
• Working examples with sample inputs and expected outputs
• Practical applications demonstrating real-world usage scenarios

🔧 Repository Structure

├── data_structures/
│   ├── arrays/array_operations.py
│   ├── linked_lists/singly_linked_list.py
│   ├── stacks/stack.py
│   ├── queues/queue.py
│   ├── trees/binary_search_tree.py
│   └── hash_tables/hash_table.py
├── algorithms/
│   ├── sorting/sorting_algorithms.py
│   └── searching/searching_algorithms.py
├── examples/example_usage.py
└── README.md (comprehensive tutorial guide)

🎯 Key Features

• Beginner-friendly: Clear explanations suitable for learning
• Interview-ready: Covers most common data structures and algorithms asked in technical interviews
• Production-quality: Clean, well-documented code with proper error handling
• Testable: All implementations include working examples and can be run directly
• Comparative: Performance analysis helps understand trade-offs between different approaches

The repository is now ready to serve students, professionals preparing for interviews, and anyone looking to understand fundamental computer science concepts through practical implementations.

Example Usage

# Quick demonstration of key features
from data_structures.stacks.stack import balanced_parentheses
from algorithms.sorting.sorting_algorithms import quick_sort
from data_structures.trees.binary_search_tree import BinarySearchTree

# Check balanced parentheses
print(balanced_parentheses("((()))"))  # True

# Sort an array
print(quick_sort([64, 34, 25, 12, 22, 11, 90]))  # [11, 12, 22, 25, 34, 64, 90]

# Use a binary search tree
bst = BinarySearchTree()
for val in [50, 30, 70, 20, 40]:
    bst.insert(val)
print(bst.inorder_traversal())  # [20, 30, 40, 50, 70] - sorted order

All implementations are thoroughly tested and ready for educational and practical use.

Original prompt

A repository that contains data structure and algorithm tutorial

---

✨ Let Copilot coding agent set things up for you — coding agent works faster and does higher quality work when set up for your repo.