How to Make a Success of Data Science | Jan Teichmann, Zoopla How to Make a Success of Data Science | Jan Teichmann, Zoopla
Diversity and Inclusion in Data Science | Noelle Silver, HackerU Diversity and Inclusion in Data Science | Noelle Silver, HackerU
Fueling Digital Transformation Within the Enterprise | Ayan Bhattacharya, Deloitte Fueling Digital Transformation Within the Enterprise | Ayan Bhattacharya, Deloitte
The Spirit of the City: Capturing Network-Generated Data for Better Cities | Luca Maria Aiello, Nokia Bell Labs The Spirit of the City: Capturing Network-Generated Data for Better Cities | Luca Maria Aiello, Nokia Bell Labs
Cleaner Oceans Through AI and Data Science | Bruno Sainte-Rose, The Ocean Cleanup Cleaner Oceans Through AI and Data Science | Bruno Sainte-Rose, The Ocean Cleanup
Quelle place occupe la data au sein du Groupe La Poste | Pierre-Etienne Bardi, Groupe La Poste Quelle place occupe la data au sein du Groupe La Poste | Pierre-Etienne Bardi, Groupe La Poste
Panel: Magical Experiences and Greater Well-Being | Panel with Disney, Warner Media, Blue Cross Blue Shield Panel: Magical Experiences and Greater Well-Being | Panel with Disney, Warner Media, Blue Cross Blue Shield
Challenges et enjeux lors de l’opérationnalisation de projets data | Showroomprivé, Dataiku Challenges et enjeux lors de l’opérationnalisation de projets data | Showroomprivé, Dataiku
AI in the B2B customer journey | José Ramón Sánchez Martos, Telefónica AI in the B2B customer journey | José Ramón Sánchez Martos, Telefónica
The data science challenges in the European SESAME project | Igor Girard-Carrabin & William Lacheny, ArianeGroup The data science challenges in the European SESAME project | Igor Girard-Carrabin & William Lacheny, ArianeGroup
Transformation data chez Engie GEM | Haga Rabekoto, ENGIE Transformation data chez Engie GEM | Haga Rabekoto, ENGIE
Ethical AI: Inclusivity as a Messy, but Promising Answer | Larry Orimoloye, Dataiku Ethical AI: Inclusivity as a Messy, but Promising Answer | Larry Orimoloye, Dataiku
How Mercedes-Benz, Vodafone, and Credit Suisse Super-Sized their Data Initiatives | A Panel How Mercedes-Benz, Vodafone, and Credit Suisse Super-Sized their Data Initiatives | A Panel
Can We Make AI Likeable? | Florian Douetteau, Dataiku Can We Make AI Likeable? | Florian Douetteau, Dataiku
The 7 Powers of Machine Learning | Lucas Bernardi, Booking The 7 Powers of Machine Learning | Lucas Bernardi, Booking
Democratizing Automated Forecasting | Lukas Stroemsdoerfer, Mercedes-Benz Democratizing Automated Forecasting | Lukas Stroemsdoerfer, Mercedes-Benz
Becoming an Intelligent Organization in the Age of AI | Jeff McMillan, Morgan Stanley Becoming an Intelligent Organization in the Age of AI | Jeff McMillan, Morgan Stanley
What’s At Stake With Your Data Projects Operationalization? | Romain Fouache, Dataiku What’s At Stake With Your Data Projects Operationalization? | Romain Fouache, Dataiku
Developing ML-Driven Customer Facing Products at Square | Marsal Gavalda, Square Developing ML-Driven Customer Facing Products at Square | Marsal Gavalda, Square
The Growing Pains, Pitfalls & Future for a Data Science Team in a Hyper-Growth Company | Shaun Moate, DAZN The Growing Pains, Pitfalls & Future for a Data Science Team in a Hyper-Growth Company | Shaun Moate, DAZN
La mise en production de vos projets data avec Docker & Kubernetes | Jean-Bernard Jansen, Dataiku La mise en production de vos projets data avec Docker & Kubernetes | Jean-Bernard Jansen, Dataiku
Which organizational strategies are required to innovate in production? | Sébastien Berard, Euronext Which organizational strategies are required to innovate in production? | Sébastien Berard, Euronext
The Law of Averages to Deal with Data Science Frustration | Adrian Badi, Demant The Law of Averages to Deal with Data Science Frustration | Adrian Badi, Demant
How to Improve and Innovate Tax Collection by Municipalities | Jan Geert Bakker, Gemeente Amsterdam How to Improve and Innovate Tax Collection by Municipalities | Jan Geert Bakker, Gemeente Amsterdam
