Please note that you are curently looking at the ongoing Academic Programs. Applications are now closed for this academic year (2020-2021) for licences, professional licences, masters, DUT and regulated health training. If you are interested for an application in 2021-2022, please click on this link for the appropriate Academic Programs.
Degrees incorporating this pedagocial element :
- Master in Computer science
- Master in Mathematics and applications
- Master Mathématiques et applications
Description
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.
Recommended prerequisite
Linear algebra, probability theory, statistics.
Targeted skills
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.
In brief
Period : Semestre 9Credits : 3
Number of hours
- CM : 18h
Contact(s)
Pierre Etore
Ronald Phlypo
