Understanding the key principles of data science.
Section 1: The History of Analytics
Section 2: From data management to artificial intelligence
This section is illustrated by various practical cases: Chatbot, Attribution & contribution, Prediction & predictive maintenance, Pattern identification, Neuronal networks
Section 3: Benchmarking of data visualisation tools
Section 4: Focus on functional architectures and enterprise organizations around data science
Our methods focus on practical feedbacks (market insights and illustrated use cases), organized around theoretical contributions.
Keynotes and theoretical contributions
Objective: Sharing common vocabulary and general principles, on business and technical points
Illustrated use cases
Objective: involving participants by projecting themselves into their daily lives, as employees or consumers
Objective: Providing insights about the key players and figures in the ecosystem
Digital & Technical Departments
Up to 6 participants