A learning-by-doing architecture where data foundations, statistical reasoning, machine learning and AI workflows connect into professional deliverables.
Explore real student projects, analytical workflows and professional deliverables.
Teaching System
Learning by doing connects every program.
Students learn by building concrete analytical deliverables.
Students learn by cleaning data, building analyses, comparing models and explaining decisions.
Choose a program
From messy spreadsheets to actionable insights.
During the course
Teach students how to structure, clean, analyze and communicate data to support strategic decisions.
Students can independently clean a dataset, identify relevant KPIs, build visualizations and defend a data-driven recommendation.
Practiced skills
Key ideas
Tools used
What students build
Which city should the company prioritise, and what evidence supports that choice?
Students clean order data, compare cities, calculate business KPIs and defend a market expansion recommendation.
Students produce
How does France evolve compared with other European countries when indicators are normalised?
Students prepare Eurostat-style datasets, build base 100 comparisons and turn macroeconomic trends into readable conclusions.
Students produce
What do the data suggest about the strategic position of a traditional manufacturer versus a tech-driven car company?
Students explore automotive datasets, create comparative visualisations and build a clear analytical narrative.
Students produce
How I teach
Students learn by building real deliverables rather than completing isolated exercises.
Every conclusion must be supported by data, reasoning and clear communication.
Projects reproduce the tools, expectations and constraints found in industry.