https://hub.imt-atlantique.fr/datascience-toolkit/course-organization/index.html Presentation & objectives This section gathers the practical information you need to navigate the course: Assessment Guidelines — how your work is evaluated and how your final grade is determined. Competency Levels — what is expected of you at each level of mastery, for each competency. Academic Integrity — the rules you must follow, including what AI tools you may and may not use. License & Citation — the terms under which this content is shared, and how to cite it. Hugo en-us https://hub.imt-atlantique.fr/datascience-toolkit/course-organization/assessment/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/course-organization/assessment/index.html Overview Your final assessment is based on the Air Quality project you build throughout Sessions 2 and 3, and on an individual supervised exam. Together, they show your ability to move from exploratory data science to structured, maintainable and well-documented machine learning work, and to explain what you have learned to someone else. Core competencies Your work will be assessed based on three fundamental competencies that align with the official program objectives: https://hub.imt-atlantique.fr/datascience-toolkit/course-organization/competencies/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/course-organization/competencies/index.html This page breaks down each of the three competencies assessed in this course into critical learnings, and describes three levels of mastery for each of them. The Competent level is the target to reach by the end of Session 3. The levels are cumulative: reaching Intermediate assumes the Novice descriptors are already acquired, and Competent assumes both. Each learning objective stated at the top of an activity page contributes to one of the critical learnings below; the “Evidenced in” line tells you which activities work on each competency. https://hub.imt-atlantique.fr/datascience-toolkit/course-organization/integrity/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/course-organization/integrity/index.html AI Tool Usage Guidelines AI Banned AI Allowed Coding an activity for you — having AI write, in whole or in part, the answer to an activity in your place. Deepening a concept — asking AI to help you understand a concept in more depth. Answering activity questions — asking AI to answer the questions posed within an activity. Understanding a mechanism — asking AI to explain how a mechanism or algorithm works. Copy/pasting into a reflection — pasting any AI-generated message into a reflection answer is treated as cheating on continuous assessment. Searching for references — asking AI to help you find references, examples, or code snippets to learn from. Hiding AI assistance — using AI without acknowledging, disclosing, or citing it as a source. Understanding documentation — asking AI to help you make sense of technical documentation. Submitting AI output as your own — copying AI-generated content and presenting it as original work. Challenging your reasoning — asking AI to question your assumptions and identify edge cases, then evaluating them yourself. Warning This framework ensures projects demonstrate technical competence and professional readiness while maintaining academic integrity standards suitable for professional portfolios. Failure to comply with the AI Banned rules will result in a report for academic fraud. https://hub.imt-atlantique.fr/datascience-toolkit/course-organization/license/index.html Mon, 01 Jan 0001 00:00:00 +0000 https://hub.imt-atlantique.fr/datascience-toolkit/course-organization/license/index.html License This content is made available under the Creative Commons Attribution-NonCommercial 4.0 International License. Citation To cite this educational material, please use the following reference: Lina Fahed, Laurent Brisson. "Data Science Toolkit and Applications". IMT Atlantique. Available under CC BY-NC 4.0 license. https://hub.imt-atlantique.fr/datascience-toolkit/ You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material Under the following terms: