Data Structures & Algorithms (DSA) Preparation 🚀

A comprehensive repository containing custom implementations of fundamental data structures and algorithmic patterns in C and Python. This workspace is designed for building computational problem-solving foundations, studying time/space complexity tradeoffs, and practicing coding interview questions.

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💡 Real-world Relevance & Problem Solving

Strong foundations in Data Structures and Algorithms (DSA) are critical to writing high-performance, resource-efficient code. In real-world software engineering, these concepts are directly applied in:

1. Operating Systems & Memory Management: Page replacement (Stacks/Queues), heap memory allocation (Heaps/Trees).
2. Database Systems: Efficient search, range queries, and indexing (B-Trees, Hash Tables, Matrix operations).
3. Networking: Routing algorithms, packets serialization, buffer management (Queues, Graphs).
4. Data Science & ML: Multi-dimensional operations, linear transformations, and distance metrics (Matrices, Arrays).

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🛠️ Tech Stack & Dev Environment

• Primary Language: C (ANSI C/C99 standard) for low-level memory control and raw performance understanding.
• Secondary Language: Python 3.x (primarily for solving LeetCode problems quickly).
• Compiler: gcc (GNU Compiler Collection).
• Workspace Settings: Integrated .vscode settings for automating compilation and debugging in Visual Studio Code.

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📂 Repository Structure

The code is categorized logically by data structure or paradigm:

• ARRAY: General array operations.
  • Reversals, rotations (left/right rotation), insertions, deletions, merging sorted arrays.
  • Moving zeroes to the end, identifying missing numbers, and finding the $1^{\text{st}}, 2^{\text{nd}}, 3^{\text{rd}}$ largest elements.
• LINKED LIST: Dynamic memory structures.
  • Singly and doubly linked lists (insertions, deletions, conversions from arrays, finding the middle node, removing the $k^{\text{th}}$ element).
• HEAP: Binary Heaps.
  • Max-Heap and Min-Heap implementations (insertion, get max/min, deletion of max/min, verification checks).
• MATRIX: 2D grid computations.
  • Matrix addition, scalar multiplication, matrix multiplication, and transposition.
• STACK (LIFO) & QUEUE (FIFO): Linear helper structures.
  • Array-based and pointer-based implementations.
• TREE: Hierarchical data structures.
  • Binary Search Tree (BST) operations (insertion, deletion, search, traversals like Inorder, Preorder, Postorder).
• SEARCH: Searching techniques.
  • Linear Search, Binary Search (iterative and recursive).
• LEETCODE: Python-based solutions for coding problems (e.g., 238. Product of Array Except Self).

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🚀 How to Run the Code

1. Compiling and Running C Files

Make sure you have gcc installed (via MinGW-w64 on Windows, or through your package manager on macOS/Linux).

To compile a specific file (e.g., 3_array_reversal.c):

gcc ARRAY\3_array_reversal.c -o array_reversal.exe

To run the compiled executable:

• Windows: .\array_reversal.exe
• macOS/Linux: ./array_reversal

2. Running Python Code

To run Python scripts (e.g., in the LEETCODE directory):

python LEETCODE\238.py