🌱 Getting Started with the Environmental Data Science Course

Welcome! This is a self-paced workshop designed to help you learn essential tools for environmental data analysis using Python and Jupyter Notebooks.

You are encouraged to search online, use ChatGPT or your favorite AI assistant, and explore additional resources to support your learning.

⚠️ Disclaimer: This course is currently in beta. You may encounter errors or inconsistencies. Please report any issues or contribute to improvements!

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📦 Required Software

Please install the following programs before starting the course:

1. Git – Version control system
   👉 Download Git

2. Anaconda or Miniconda – Python environment manager
   👉 Download Anaconda

3. Visual Studio Code (VS Code) – Code editor and notebook interface
   👉 Download VS Code

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⚙️ Setup Instructions

Follow these steps after installing the required programs:

1. Clone this repository:

git clone https://github.com/gilbertoCM/env_data_analysis_course.git
cd env_data_analysis_course

2. Create the Conda environment:

conda env create -f environment.yml
conda activate environmental_python

This creates an environment called environmental_python with all the necessary packages for data science and geospatial analysis.

To update the environment later:

conda env update --file environment.yml --prune

3. Install pre-commit tools to clean Jupyter notebooks:

pip install pre-commit
pre-commit install

This ensures your notebooks stay clean by automatically removing outputs and metadata before each commit.

4. Open the folder in VS Code and install the recommended extensions when prompted:

   • Python
   • Jupyter
   • Black Formatter
   • GitHub Copilot
   • Rainbow CSV

5. Test your setup by creating a new notebook and running:

print("Hello, world!")

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🧪 Running the Course Notebooks

All notebooks are in the scripts/ folder.
You can run them using:

jupyter lab

Or directly from VS Code.

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📁 Folder Structure

The repository is organized into the following folders:

• /data — Contains the CSV database that will be used during the course.
• /documentation — Includes the course syllabus and exercise descriptions.
• /manuals — Helpful manuals and guides for Git, Python, and data analysis.
• /output_files — Stores generated outputs such as PDF files and results.
• /scripts — This is the main folder where you will work with Jupyter notebooks and Python scripts during the course.

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🧼 Notebook Version Control

This project uses nbstripout and pre-commit to:

• Automatically remove outputs and metadata from notebooks
• Keep Git commits clean and easy to review

No additional setup is needed once you run pre-commit install.

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👨‍🏫 Course Author

This course was developed by:

José Gilberto Cardoso Mohedano
Email: gcardoso@cmarl.unam.mx

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