This repository serves as a comprehensive learning resource for Python programming and data science, targeting novices to seasoned developers. It aims to provide curated tutorials, exercises, and real-world examples to foster Python proficiency and data science expertise, alongside practical advice for career development.

How It Works

The repository is structured into two main sections: "Python Coding" and "Data Science." Python Coding covers fundamental concepts, built-in functions, libraries (NumPy, Pandas), APIs, and advanced topics like decorators and virtual environments. Data Science delves into data exploration, web scraping, transit data analysis, data preprocessing, time series forecasting, and machine learning model building. The content is primarily delivered through Jupyter Notebooks, with links to specific topics and exercises.

Quick Start & Requirements

• Installation: No direct installation is required for the repository content itself. Users are encouraged to use tools like Google Colab, Jupyter Notebook, or Visual Studio Code.
• Prerequisites: Python installation is recommended. Specific libraries like NumPy and Pandas are used within the notebooks.
• Resources: Google Colab is suggested for interactive learning and tutorial creation. Links to relevant notebooks and GitHub directories are provided for exploration.

Highlighted Details

• Extensive coverage of Python fundamentals, libraries (NumPy, Pandas), and APIs.
• Dedicated sections for data science, including data exploration, web scraping, and machine learning.
• Includes interview preparation materials and practical coding exercises.
• Content is presented primarily through Jupyter Notebooks, facilitating interactive learning.

Maintenance & Community

The repository is maintained by Tanu Nanda Prabhu and has approximately 20 contributors. Users are encouraged to contribute by forking the repository. Links to Reddit communities for Python are provided.

Licensing & Compatibility

The repository does not explicitly state a license. The content is presented as educational material, and users are encouraged to contribute.

Limitations & Caveats

The repository does not specify a license, which may impact commercial use or redistribution. While Google Colab is recommended, some notebooks might require specific library versions or local setups not detailed.