https://hub.imt-atlantique.fr/datascience-toolkit/tutorials/index.html Presentation & objectives This section provides essential tutorials to set up your development environment and master version control for data science projects. These tutorials establish the foundation for professional data science workflows, ensuring you have the proper tools and collaborative skills needed for modern data science development. Learning Objectives: Configure a modern Python development environment with uv package manager Set up Visual Studio Code with essential extensions for data science Master Git fundamentals and collaborative workflows using GitLab Understand modern Python project standards with pyproject.toml Implement professional development practices and best practices Prerequisites: Hugo en-us https://hub.imt-atlantique.fr/datascience-toolkit/tutorials/environment/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/tutorials/environment/index.html Duration30min Introduction Setting up a proper development environment is crucial for data science work. This tutorial guides you through installing uv, the modern Python package manager, and understanding modern Python project standards with pyproject.toml configuration. A well-configured environment ensures: Reproducibility: Your code works consistently across different machines Speed: Fast dependency installation and resolution Simplicity: No manual virtual environment management Modern Standards: Following current Python development practices Prerequisites Basic command line knowledge Administrator/sudo access on your machine Internet connection for package downloads Development Environment Setup Visual Studio Code Installation Visual Studio Code (VS Code) is the recommended IDE for this course. It provides excellent Python support, integrated terminal, and extensions for data science. https://hub.imt-atlantique.fr/datascience-toolkit/tutorials/gitlab_1/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/tutorials/gitlab_1/index.html Duration1h30 Introduction This tutorial is a hands-on collaborative exercise that brings Git and GitLab concepts to life through a simple “Hello World” project. You’ll work with a classmate to experience real-world collaboration scenarios including conflicts, branching, and merge requests. Each student plays both roles: 🅰️ Alice: Create and manage your own project 🅱️ Bob: Collaborate on your partner’s project Now: Find a classmate to partner with Exchange GitLab usernames You can work asynchronously (coordinate timing via messages) Prerequisites Readings You must read these comprehensive guides before starting: https://hub.imt-atlantique.fr/datascience-toolkit/tutorials/gitlab_2/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/tutorials/gitlab_2/index.html Commit Frequency Make incremental commits as you develop your analysis: After completing data cleaning After major feature engineering steps After generating each key visualization After updating documentation Commit Messages Use descriptive messages that explain: What analysis steps were completed Which insights were discovered What visualizations were created How documentation was updated File Organization Ensure your final commit includes: Clean, well-documented notebook High-quality exported visualizations Updated README with findings Proper .gitignore configuration