https://github.com/saadsalmanakram/ds-bigo-complexity

It's all about Data Structures... This repository is designed to provide a comprehensive guide to understanding, implementing, and mastering data structures from the ground up.
https://github.com/saadsalmanakram/ds-bigo-complexity

bigonotation data-structures datastructures datastructures-algorithms timecomplexity

Last synced: 7 months ago
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It's all about Data Structures... This repository is designed to provide a comprehensive guide to understanding, implementing, and mastering data structures from the ground up.

Host: GitHub
URL: https://github.com/saadsalmanakram/ds-bigo-complexity
Owner: saadsalmanakram
Created: 2024-08-30T06:23:32.000Z (about 1 year ago)
Default Branch: main
Last Pushed: 2025-04-05T14:41:19.000Z (7 months ago)
Last Synced: 2025-04-05T15:30:46.315Z (7 months ago)
Topics: bigonotation, data-structures, datastructures, datastructures-algorithms, timecomplexity
Homepage:
Size: 94.7 KB
Stars: 2
Watchers: 1
Forks: 0
Open Issues: 0
Metadata Files:
- Readme: README.md

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README

          
---

# 📚 DS-BigO-Complexity  

![Algorithms and Complexity](https://cdn.pixabay.com/photo/2023/02/08/08/50/frequency-wave-7776034_1280.jpg)  

## 📝 Introduction  

**DS-BigO-Complexity** is a comprehensive guide to understanding **Data Structures, Algorithms, and Time Complexity**. This repository is designed for beginners and intermediate learners who want to strengthen their problem-solving skills and optimize their code for efficiency.  

🔹 Learn about common **data structures** like arrays, linked lists, trees, graphs, and heaps.  

🔹 Implement and analyze **sorting and searching algorithms**.  

🔹 Master **Big-O Notation** and understand **time & space complexity**.  

🔹 Solve real-world **coding problems** with optimized solutions.  

---

## 🚀 Features  

- **In-depth explanations** of fundamental data structures  

- **Hands-on implementations** in Python  

- **Big-O Complexity Analysis** for each algorithm  

- **Visual representations** of sorting & searching algorithms  

- **Interview-style problems** with optimal solutions  

- **Practice problems & challenges**  

---

## 📌 Prerequisites  

Before getting started, ensure you have:  

- **Python 3.x** installed → [Download Here](https://www.python.org/downloads/)  

- Basic knowledge of programming concepts  

---

## 📂 Repository Structure  

```

DS-BigO-Complexity/

│── data_structures/        # Implementations of stacks, queues, linked lists, trees, etc.

│── algorithms/             # Sorting, searching, dynamic programming, graph algorithms

│── complexity_analysis/    # Big-O notation, best-case, worst-case, average-case analysis

│── interview_questions/    # Common DSA questions from tech interviews

│── visualizations/         # Graphical representations of sorting & searching algorithms

│── README.md               # Project documentation

└── requirements.txt        # Python dependencies

```

---

## ⏳ Understanding Big-O Notation  

Big-O notation is used to classify algorithms according to their **runtime complexity**. Here’s a quick reference:  

| Complexity | Name | Example |

|------------|-----------------|--------------------------------|

| O(1) | Constant Time | Accessing an array index |

| O(log n) | Logarithmic Time | Binary search |

| O(n) | Linear Time | Iterating through an array |

| O(n log n) | Linearithmic Time | Merge Sort, Quick Sort |

| O(n²) | Quadratic Time | Nested loops (e.g., Bubble Sort) |

| O(2ⁿ) | Exponential Time | Fibonacci recursion |

| O(n!) | Factorial Time | Traveling Salesman Problem |

---

## 🛠️ Example: Implementing a Sorting Algorithm  

Here’s an implementation of **Merge Sort**, an O(n log n) sorting algorithm:  

```python

def merge_sort(arr):

    if len(arr) <= 1:

        return arr

    

    mid = len(arr) // 2

    left_half = merge_sort(arr[:mid])

    right_half = merge_sort(arr[mid:])

    

    return merge(left_half, right_half)

def merge(left, right):

    sorted_arr = []

    i = j = 0

    

    while i < len(left) and j < len(right):

        if left[i] < right[j]:

            sorted_arr.append(left[i])

            i += 1

        else:

            sorted_arr.append(right[j])

            j += 1

    

    sorted_arr.extend(left[i:])

    sorted_arr.extend(right[j:])

    

    return sorted_arr

# Example Usage

arr = [5, 3, 8, 6, 2, 7, 4, 1]

print("Sorted Array:", merge_sort(arr))

```

Output:  

```

Sorted Array: [1, 2, 3, 4, 5, 6, 7, 8]

```

---

## 🔍 Topics Covered  

### 📌 **Data Structures**  

- **Arrays & Strings**  

- **Linked Lists** (Singly, Doubly, Circular)  

- **Stacks & Queues**  

- **Hash Tables & Hash Functions**  

- **Trees & Binary Search Trees (BSTs)**  

- **Heaps & Priority Queues**  

- **Graphs (Adjacency List & Matrix)**  

### 🔢 **Algorithms**  

- **Sorting Algorithms:** Bubble, Selection, Insertion, Merge, Quick, Heap Sort  

- **Searching Algorithms:** Linear Search, Binary Search  

- **Graph Algorithms:** BFS, DFS, Dijkstra’s Algorithm  

- **Dynamic Programming:** Fibonacci, Knapsack Problem  

- **Divide & Conquer:** Merge Sort, Quick Sort  

- **Greedy Algorithms:** Huffman Coding, Activity Selection  

- **Backtracking:** N-Queens, Sudoku Solver  

---

## 🏆 Problem-Solving Challenges  

📌 The repository includes **real-world problems** with optimal solutions. Examples:  

1️⃣ Find the **two numbers** in an array that sum up to a target (`O(n)`)  

2️⃣ Detect a **cycle** in a linked list (`O(n)`)  

3️⃣ Find the **lowest common ancestor (LCA)** in a BST (`O(log n)`)  

4️⃣ Implement a **LRU Cache** using a HashMap & Doubly Linked List (`O(1)`)  

5️⃣ Solve the **N-Queens problem** using Backtracking (`O(N!)`)  

---

## 🔥 Sorting Algorithm Visualizations  

The repository includes **sorting visualizations** for algorithms like:  

| Algorithm | Time Complexity | Visualization |

|-----------|----------------|---------------|

| Bubble Sort | O(n²) | ![Bubble Sort](https://upload.wikimedia.org/wikipedia/commons/c/c8/Bubble-sort-example-300px.gif) |

| Merge Sort | O(n log n) | ![Merge Sort](https://upload.wikimedia.org/wikipedia/commons/thumb/e/e6/Merge_sort_algorithm_diagram.svg/600px-Merge_sort_algorithm_diagram.svg.png) |

| Quick Sort | O(n log n) | ![Quick Sort](https://upload.wikimedia.org/wikipedia/commons/6/6a/Sorting_quicksort_anim.gif) |

---

## ⚡ How to Run the Code  

### 1️⃣ Clone the Repository  

```bash

git clone https://github.com/saadsalmanakram/DS-BigO-Complexity.git

cd DS-BigO-Complexity

```

### 2️⃣ Install Dependencies  

```bash

pip install -r requirements.txt

```

### 3️⃣ Run Example Scripts  

```bash

python algorithms/merge_sort.py

python data_structures/linked_list.py

```

---

## 🏆 Contributing  

Contributions are welcome! 🚀  

🔹 **Fork** the repository  

🔹 Create a new branch (`git checkout -b feature-name`)  

🔹 Commit changes (`git commit -m "Added new sorting algorithm"`)  

🔹 Push to your branch (`git push origin feature-name`)  

🔹 Open a pull request  

---

## 📜 License  

This project is licensed under the **MIT License** – feel free to use, modify, and share the code.  

---

## 🔗 Resources & References  

- [Big-O Complexity Cheat Sheet](https://www.bigocheatsheet.com/)  

- [MIT Algorithms Course](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/)  

- [CLRS: Introduction to Algorithms](https://en.wikipedia.org/wiki/Introduction_to_Algorithms)  

---

## 📬 Contact  

For queries or collaboration, reach out via:  

📧 **Email:** saadsalmanakram1@gmail.com  

🌐 **GitHub:** [SaadSalmanAkram](https://github.com/saadsalmanakram)  

💼 **LinkedIn:** [Saad Salman Akram](https://www.linkedin.com/in/saadsalmanakram/)  

---

⚡ **Master Data Structures & Algorithms with Confidence!** ⚡  

---

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