A new Business Case partnership between the students of Troyes University of Technology and NEOMA BS
Published on 10/19/2018
Thematics :
A new Business Case partnership between the students of Troyes University of Technology and NEOMA BS
Published on 10/19/2018
Future data scientists and marketers from the two schools have joined forces for an innovative educational project. An initiative proposed by these two members of the Conférence des Grandes Écoles (CGE) and in association with the Converteo company.
On October 11, students from the Advanced Master in "Expert Big Analytics et Métriques" (UTT) and "Marketing et Data Analytics" (NEOMA BS) joined forces to work together on a common project. The students from these two high-level professional training courses are to work together over a 4-month period on a business case proposed this year by the digital and data consulting firm, Converteo. The teaching objective of the project is to have 5 mixed marketing and IT/data teams work together from October 2018 to February 2019. This collaborative approach aims to incorporate the entire data competence chain (data engineering, data science, analysis, recommendation) to prepare students from both of these Advanced Master's courses for the realities of the professional world they will come up against at the end of their studies.
"One of the aims of the project we are running with the UTT and Converteo is to place students in a situation as close as possible to what they will experience in the reality of the professional world. It will be rewarding for our students to work alongside data scientists, and this joint project will allow them to find a common solution to the initial problem. The creation of this project is both mutually rewarding and a fantastic knowledge-sharing opportunity," points out Françoise Collard, Head of the MS Marketing et Data Analytics.
Babiga Birregah, Senior Lecturer and Head of the Advanced Master's in "Expert Big Analytics et Métriques" adds: "The interest for our students to work with marketing specialists is twofold. Firstly, they will be able to develop their agility in breaking down barriers and therefore rely on each of their team member's potential. Secondly, they learn to share their Data Science skills in all areas to solve extremely concrete problems. For us, it is one way to help them experience and anticipate the "Problem Team - Solution Team" roles so frequently encountered during the various phases of a Big Data project."