- Understand the foundations of applied Data Science for text and documents
- • Master embeddings, semantic similarity, and vectorisation
- Build an end-to-end RAG pipeline locally: ingestion, chunking, indexing, retrieval, generation
- Understand LLM limitations, hallucination sources, and quality-control mechanisms
- Apply RAG techniques to a realistic, compliance-focused business scenario
Expected Deliverables :
- A fully functioning low-cost RAG pipeline (script/GitHub)
- • A structured vector index (embeddings + metadata)
- • A simple architecture diagram of the pipeline
- • A mini technical report with design decisions and recommendations
6. Strategic Digital Architecture (Valentin Dirken) 18/05, 19/05 & 20/05
Learning outcomes :
- Diagnose operational inefficiency — identify manual processes that erode margins and slow growth
- Connect tools via integration — eliminate data silos and enable real-time operational visibility
- Design a phased technology roadmap — build implementation strategy aligned with business growth
- Build an IT financial model and board-ready pitch deck with ROI
Expected Deliverables :
- Process Automation Map + Automation Backlog
- Data Requirements Schema + Data Connectivity Matrix
- Tools + Integration Blueprint + Real-Time Data Flow Diagram
- Implementation Roadmap + RACI Matrix + SLAs • Board-Ready Pitch Deck (5 slides)
7. From DATA to prediction (Nathalia Garcia Colin) 25/05, 26/05 & 27/05
Learning outcomes :
• The Core Concept: Why graphs matter and what makes GNNs different from other AI approaches
• Real-World Applications: From fraud detection in financial networks to drug discovery, supply chain optimization, and social media recommendations
• Industry Use Cases: How companies like Pinterest, Uber, and pharmaceutical firms use GNNs to solve complex problems
• Research Frontiers: Current breakthroughs in materials science, protein folding, and knowledge graphs
• Business Value: When to consider GNNs versus traditional approaches, and what resources they require
• Limitations & Future: What GNNs can't do well (yet) and where the field is headed
Expected Deliverables
By the end, you'll recognize opportunities where GNN technology could provide competitive advantage, speak confidently with technical teams about GNN capabilities, and make informed decisions about investing in graph-based AI solutions.
8. AI Under EU Digital Law (Valentina Dalla Giovanna) 01/06, 02/06 & 03/06
Learning outcomes :
- Understand the EU Digital Regulatory Framework and what matters in practice
- Navigate the four core legal pillars: Digital Services, Privacy & Data, Cybersecurity, AI rules
- Know your rights and responsibilities under GDPR and the Data Act
- Prepare for the AI Act and broader liability framework
Expected Deliverables :
- Personal compliance roadmap for your organization
- Checklist of applicable regulations
- Resource guide for ongoing compliance
9. Responsible Digital (Jules Delcon) 15/06, 16/06 & 17/06
Learning outcomes :
- Discover the foundations of sustainable development (LCA, SDGs, CSR)
- Define the social and environmental impacts of digital technologies
- Understand the hierarchy of environmental impacts of digital equipment and usage
- Discover methodology and tools for implementing responsible digital practices
- Develop levers and tools to deploy a Responsible Digital strategy
- Give SMEs practical keys to make better digitalization choices
Expected Deliverables :
- Personal digital eco-gestures checklist
- Digital wellbeing policy template
- Responsible Digital action plan for your organisation
- Resource guide with tools and references