Python Tutorial for Data Science

This repository is a comprehensive guide for learning data science using Python. It covers various essential libraries and tools commonly used in the field of data science, including Jupyter Notebook, Matplotlib, NumPy, Pandas, Scikit-learn, and PyTorch.

Contents

1. Jupyter Notebook: Introduction to Jupyter Notebook and its features.

2. Matplotlib: Exploring data visualization using Matplotlib library.

3. NumPy: Introduction to NumPy and its powerful array operations for numerical computing.

4. Pandas: Understanding data manipulation and analysis with Pandas.

5. Scikit-learn: Introduction to machine learning with Scikit-learn library.

6. PyTorch: Getting started with deep learning using PyTorch framework.

Usage

1. Clone the repository using the following command:

   bashCopy code

   git clone https://github.com/NagiPragalathan/Python_Tutorial_For_Data-Science.git

2. Install the required dependencies using pip:

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   pip install -r requirements.txt

3. Navigate to the desired notebook in the notebooks directory and open it using Jupyter Notebook.

4. Follow the instructions and code examples provided in each notebook to learn and practice data science concepts.

Contributing

If you find any issues or have suggestions for improvement, please feel free to contribute to this repository. You can submit bug reports, feature requests, or pull requests to help enhance the learning experience for everyone.

License

This project is licensed under the MIT License. Feel free to use and modify the code for educational purposes.

Let's dive into the exciting world of data science and explore the power of Python together!