The ChessAIThon project (2025-1-ES01-KA220-VET-000354329) is co-funded by the European Union. The views and opinions expressed in this publication are those of the author(s) only and do not necessarily reflect those of the European Union or the Spanish Service for the Internationalisation of Education (SEPIE). Neither the European Union nor the National Agency SEPIE can be held responsible for them.

Chess data: formats structures and dataset files, control version

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Use Notebooks

Introduce Google Colab Notebooks as the central, collaborative workspace for the entire AI project.

Explain that a Notebook seamlessly combines code execution, documentation, and visual output into a single environment, which is ideal for data science and AI.

Project Stages in the Notebook

Students will use the Notebook for every stage of the project:

  • Exploration: They'll start by using Pandas to load and explore raw CSV data, quickly visualizing initial patterns.
  • Transformation (ETL): The Notebook is where they execute the core algorithmic logic, converting data into the specialized 77x8x8 Parquet format using NumPy and Python Chess. The interspersed explanatory text and code blocks make it easy to follow the complex transformation pipeline.
  • Collaboration: Since Colab or Kaggle is cloud-based, multiple students can work on different sections or view the master ETL process simultaneously, fostering peer learning.
  • Integration: The Notebook acts as the bridge, running system commands to interact with Git (for version control) and directly integrating with external platforms like Kaggle and Hugging Face to pull datasets.

Using Notebooks teaches students the professional, interactive environment used by data scientists globally, making abstract code and data instantly visible and manageable.