Complete Python Bootcamp

Description:

Complete Python 3 Bootcamp is a repository containing the code and materials for the "Complete Python 3 Bootcamp" course offered by Pierian Data. This course is designed to teach Python programming from the ground up, covering basic to advanced topics. The repository includes Jupyter notebooks and Python scripts that correspond to the course content, enabling learners to follow along with practical examples and exercises.

Features:

1. Course Material:

   • The repository contains the coding exercises and examples used in the "Complete Python 3 Bootcamp" course. It is designed to help learners understand Python programming through hands-on practice and real-world examples.

2. Jupyter Notebooks:

   • The primary format for the course material is Jupyter notebooks (*.ipynb). These notebooks provide an interactive way to learn Python, combining code, explanations, and visualizations in a single document.

3. Python Scripts:

   • In addition to notebooks, the repository includes Python scripts (*.py) for various exercises and examples. These scripts can be executed independently of Jupyter notebooks.

4. Comprehensive Coverage:

   • The course covers a broad range of Python topics, including:
     • Basic syntax and data types
     • Control flow and functions
     • Object-oriented programming
     • Modules and libraries
     • File handling
     • Advanced topics like decorators, generators, and more

5. Hands-On Practice:

   • The repository emphasizes hands-on practice with coding exercises and projects. Learners are encouraged to modify and experiment with the provided code to deepen their understanding.

6. Open for Contributions:

   • While the primary focus is on course material, the repository is open for contributions. Users can suggest improvements, fix issues, or contribute additional exercises.

Requirements:

• Python Version:

  • The code is compatible with Python 3.x. It is recommended to use the latest stable version of Python 3 to ensure compatibility with modern libraries and features.

• Dependencies:

  • Various Python libraries are used in the course, such as:

    • numpy for numerical operations
    • pandas for data manipulation
    • matplotlib for plotting and visualization
    • jupyter for running notebooks

• Installation and Setup:

  • To use the course material, clone the repository and install the necessary dependencies.

• Development Tools:

  • Jupyter Notebook or JupyterLab is used to interact with the notebooks.

     

• Testing and Running Code:

  • The notebooks can be opened and run directly within Jupyter Notebook or JupyterLab. Python scripts can be executed from the command line or within an IDE.

• Contribution Guidelines:

  • If you want to contribute, follow the contribution guidelines specified in the CONTRIBUTING.md file or README. This includes instructions for suggesting changes, fixing issues, or adding new content.

Instructions:

Follow all the information and instructions on getting started.