https://hub.imt-atlantique.fr/datascience-toolkit/index.html Presentation & objectives This course covers the entire data science pipeline, from initial data exploration to the implementation and evaluation of machine learning models. It emphasizes hands-on experience with essential tools and libraries, preparing students to tackle real-world data science challenges. The course also incorporates best practices in software development and version control. Key concepts covered: Python programming for data science (including key libraries like Pandas, NumPy, Scikit-learn) Data manipulation and storage (structured data formats, databases) Data exploration and preprocessing techniques Data visualization principles and tools Model training and evaluation Software development best practices (version control, testing, documentation) Prerequisites : Hugo en-us Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session1/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session1/index.html Presentation & objectives By this case study, you will be able to: Set up Python environment and VS Code, using environment management tools like virtualenv or conda Load and explore a dataset using Python basics and Pandas, including basic DataFrame operations Implement error handling when loading and exploring a dataset Perform simple aggregations on a dataset Create basic visualizations with Matplotlib Implement simple linear regression for prediction Use Git for version control Very useful documentations can be found here: https://hub.imt-atlantique.fr/datascience-toolkit/session2/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session2/index.html Presentation & objectives This session focuses on implementing complete data science workflows, from exploratory analysis in notebooks to production-ready machine learning systems. Students will learn to handle real-world data challenges including time-series analysis, geographic data processing, and the critical transition from experimental code to structured, maintainable ML pipelines. Assessment Information This session contributes to your final assessment through two complementary mini-projects. For detailed evaluation criteria and requirements, please refer to the Assessment Guidelines section. https://hub.imt-atlantique.fr/datascience-toolkit/session3/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/session3/index.html Presentation & objectives This session focuses on applying machine learning techniques to Natural Language Processing (NLP) challenges through hands-on spam detection projects. Students will learn to transform unstructured text data into actionable machine learning solutions while mastering the transition from experimental notebooks to production-ready NLP pipelines. Through practical experience with authentic SMS and email datasets, you will explore the unique challenges of text classification including tokenization strategies, feature extraction techniques, cross-domain adaptation, and the critical importance of proper evaluation in imbalanced text datasets. https://hub.imt-atlantique.fr/datascience-toolkit/tutorials/index.html Mon, 01 Jan 0001 00:00:00 +0000 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: https://hub.imt-atlantique.fr/datascience-toolkit/teacher/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/teacher/index.html This section is exclusively for teachers (not accessible to students). Here you will find specific information for the teaching team. Corrections Sales of a high-tech and multimedia shop Problem understanding https://hub.imt-atlantique.fr/datascience-toolkit/assessment/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/assessment/index.html Overview Your final assessment consists of two complementary mini-projects that demonstrate your mastery of machine learning engineering principles across different domains. Both projects emphasize the transition from exploratory data science to production-ready ML systems, showcasing your ability to build structured, maintainable, and well-documented machine learning solutions. Core competencies Your work will be assessed based on three fundamental competencies that align with the official program objectives: [BC04] Software Engineering Best Practices: Apply professional development principles to data science workflows, ensuring code quality, maintainability, and effective collaboration. [BC07] End-to-End ML Pipeline Design: Design and implement complete, production-ready machine learning pipelines using modern MLOps practices and industry-standard tools. [BC01] Data-Driven Communication: Transform technical analysis into clear, actionable insights suitable for both technical and business audiences through effective visualization and statistical analysis. Read carefully the expectations for these different competencies. https://hub.imt-atlantique.fr/datascience-toolkit/integrity/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/integrity/index.html Team Collaboration Policy Projects are pair work between two students. Discussion of concepts and approaches is encouraged within teams and across the class. Code sharing between different teams results in automatic failure for all involved parties. All external sources must be properly cited. AI Tool Usage Guidelines AI Banned AI Allowed AI usage depends on activity badges. Using AI during AI Banned activities results in automatic course failure. For AI Allowed activities, teams must maintain records of AI interactions and demonstrate genuine understanding of AI-generated code. Documentation Requirements: Each project README must include a section describing AI tool usage, specifying which tools were used and for what purposes. Teams not using AI must explicitly state this. Failure to document AI usage properly may result in academic integrity violations.