This repository is dedicated to building a strong foundation in algorithmic problem-solving and computational logic. It includes practical Jupyter Notebooks covering essential topics such as Big O Notation, Data Structures, and Logarithms, the building blocks for mastering algorithms and improving coding efficiency.
| No. | Topic | Description |
|---|---|---|
| 1 | Big O Notation | Learn how to analyze time and space complexity to evaluate algorithm efficiency. |
| 2 | Data Structures | Explore common data structures (arrays, stacks, queues, linked lists) and their role in algorithm design. |
| 3 | Logarithms | Understand logarithmic growth, complexity, and their applications in searching and divide-and-conquer algorithms. |
The goal of this repository is to:
- Develop logical thinking and systematic problem-solving skills.
- Strengthen understanding of algorithm efficiency and data handling.
- Serve as a reference for students learning DSA (Data Structures and Algorithms) or preparing for coding interviews.
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Clone the repository:
git clone https://github.com/SaifRasool92/The_Algorithmic_Thinking.git
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Open any
.ipynbfile in Jupyter Notebook or VS Code. -
Follow the explanations, run the code examples, and experiment with your own variations.
- Recursion and Divide-and-Conquer
- Sorting and Searching Algorithms
- Graphs and Trees
- Dynamic Programming
