Understanding the key principles of data science.

Section 1: The History of Analytics

• The evolution of data storage & processing
• From brick counting to data management

Section 2: From data management to artificial intelligence

• Key concepts of data management: data collecting & modelling
• The challenges of data visualisation tools
• Advanced analytics & success factors
• Machine Learning & artifical 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

Market insights

Objective: Providing insights about the key players and figures in the ecosystem