Data Fundamentals
Data has become the "core asset" of modern enterprises. This first training session aims to provide a complete overview of the Data ecosystem. We will briefly trace the history of data to understand technological breakthroughs (Big Data, Cloud) and clearly define often confused concepts like Artificial Intelligence, Machine Learning, and GenAI.
Beyond definitions, this course addresses team structuring (roles, product organization) and technical architectures (Data Mesh, Data Fabric). It's the ideal module for acquiring a solid technical foundation and understanding how a company truly becomes "Data-Driven".
What you will learn
- Master essential vocabulary: from descriptive to prescriptive analytics, including AI and Machine Learning
- Understand the evolution of data architectures (OLTP vs OLAP, Data Lake, Lakehouse...) and new paradigms like Data Mesh
- Identify key roles in a data organization (Data Engineer, Data Scientist, Product Owner...) and the concept of "Data Product"
- Understand concrete AI and Data use cases in different sectors (Healthcare, Finance, Industry...)
Prerequisites
- No technical prerequisites
- Curiosity for digital topics and business challenges is recommended
Target audience
- All employees wishing to understand data challenges: Managers, Project Managers, Business roles (Marketing, HR, Finance), CTO and Tech teams, and decision-makers.
Training Program
2 modules to master the fundamentals
Topics covered
- →Brief history of data and the 4 major technological breakthroughs
- →The 4 big challenges: Volume, Variety, Velocity, Veracity
- →The analytical maturity scale: from descriptive to prescriptive
- →Demystifying AI: differences between AI, Machine Learning, Deep Learning and Generative AI (LLM)
Topics covered
- →Data activity organizations: data factory, data governance, data mesh
- →Data professions: Who does what? (Data Engineer, Data Scientist, Data Analyst, CDO)
- →The concept of "Data Product": managing data as a product with a lifecycle
- →Data Platforms: Understanding the evolution of data platforms and tools (from ETL/ELT to BI and AI-GenAI)
Activities
Collaborative workshop: Determine the organization's data maturity and sketch a conceptual data platform
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Quiz / MCQ — Quick knowledge check (paper-based or digital via tools like Kahoot/Klaxoon).
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
No date suits you?
We regularly organize new sessions. Contact us to find out about upcoming dates or to schedule a session at a date of your choice.
Register for a custom dateTrain multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote