UE Data science seminar

Informations aux utilisateurs

Veuillez noter que vous consultez une page du catalogue de formation 2020-2021. Le recrutement est actuellement terminé pour les licences, licences professionnelles, masters, DUT et formations réglementées de santé. Pour consulter le catalogue des formations 2021-2022, cliquez sur le lien suivant.

Diplômes intégrant cet élément pédagogique :


Our master programs now include a series of 6 or 7 seminars given by active researchers in the field of data processing methods and analysis.

These seminars are intended to give students some insights on modern problems and solutions developed in a data science framework, with applications in a variety of fields.

In order to make these seminars a most valuable experience for all students, a scientific paper dealing with the topic of the seminar will be selected by the speaker and dispatched to all students about 2 weeks before the seminar. Students are expected to read and study this paper, and to prepare questions, before attending the seminar. Presence at the seminars is compulsory for master students. 

The seminars will be on Thursdays around 3:30PM (no sooner).

Follow the announcements on https://data-institute.univ-grenoble-alpes.fr/education/data-science-seminar-series/ (regularly updated)

Evaluation :

At the end of the seminar series, some oral exam is organized. One of the topic presented during the seminars is randomly assigned to each student a few days in advance. The oral exam consists in a 25 min summarized presentation of the scientific issues that were addressed, and a 15 min session of discussion and questions.

A second different topic is chosen by the student, and he.she must write a report on that topic, based on the seminar and associated articles.

Pré-requis recommandés

Linear algebra, probability theory, statistics.

Compétences visées

At the end of the course, the student will be able to efficiently read and summarize seminar presentations and articles. He.she will acquire new skills and academic knowledge in data science.

Informations complémentaires

Méthode d'enseignement : En présence
Langue(s) : Anglais