The objective of the M2 SDS program is to provide essential statistical and computational skills to manage, analyze, and interpret high-dimensional data in the field of Health, in order to address key questions in Public Health and Biomedical Sciences. Courses are taught in French.
Information
Skills
The skills to be acquired lie at the intersection of three fields: medical informatics (databases, programming, etc.); data analysis (statistical methods, etc.); and biomedicine (genetics, genomics, biomarkers, imaging).
Upon completion of the program, students will be able to:
- Design a study using high-dimensional (big) data.
- Manage the entire process from the acquisition of raw data to extracting relevant information and interpreting it.
- Appropriately apply tools and methods from statistical modeling, machine learning, and high-dimensional decision-making.
Objectives
***As courses are taught in French, students must have at least a C1 level in French.***
Face à la demande croissante de professionnels dits « Data scientist » dans le domaine bio-médical, liée en partie à l’essor des nouvelles biotechnologies en génétique/génomique et à l’accès aux grandes bases de données épidémiologiques ou médico-administratives, le parcours M2 Sciences des Données de Santé (SDS) propose de former des étudiants désireux de comprendre les méthodes d'analyse de données bio-médicales en grande dimension (prédictives, explicatives, typologiques) et d'appliquer ces méthodes à des problèmes réels en Santé.
Career Opportunities
Career prospects
Chef de projet
Ingénieur de recherche ou d'études
data scientist
Further Study Opportunities
Doctorat
Fees and scholarships
The amounts may vary depending on the programme and your personal circumstances.
Admission Route
Capacity
Available Places
Target Audience and Entry Requirements
Students from the Health field (or with a strong knowledge of it), who have prior training in probability, statistics, and biostatistical modeling (in a first-year Master’s in Public Health or an equivalent program), and who wish to understand high-dimensional data analysis methods (predictive, explanatory, typological) and apply them to real-world Health problems.
As courses are taught in French, students must have at least a C1 level in French.
Application Period(s)
From 01/05/2026 to 01/08/2026
Supporting documents
Compulsory supporting documents
Motivation letter.
All transcripts of the years / semesters validated since the high school diploma at the date of application.
Certificate of French (compulsory for non-French speakers).
Curriculum Vitae.
Detailed description and hourly volume of courses taken since the beginning of the university program.
Document indicating the list of local M2 choices available here : https://urlz.fr/i3Lo.
Additional supporting documents
VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).
Recommendation letters.
Supporting documents :
- Residence permit stating the country of residence of the first country
- Or receipt of request stating the country of first asylum
- Or document from the UNHCR granting refugee status
- Or receipt of refugee status request delivered in France
- Or residence permit stating the refugee status delivered in France
- Or document stating subsidiary protection in France or abroad
- Or document stating temporary protection in France or abroad.
| Subjects | ECTS | Semester | Lecture | directed study | practical class | Lecture/directed study | Lecture/practical class | directed study/practical class | distance-learning course | Project | Supervised studies |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Stage | 30 | Semestre 2 | |||||||||
| Subjects | ECTS | Semester | Lecture | directed study | practical class | Lecture/directed study | Lecture/practical class | directed study/practical class | distance-learning course | Project | Supervised studies |
|---|---|---|---|---|---|---|---|---|---|---|---|
| UE Obligatoires | |||||||||||
| Études d'association et de liaison en génétique humaine | 3 | Semestre 1 | 30 | ||||||||
| Recherche biomédicale, études prédictives, analyse de survie | 4 | Semestre 1 | 30 | ||||||||
| Logiciels pour la génétique | 2 | Semestre 1 | 21 | ||||||||
| Méthodes et applications en pharmaco épidémiologie | 1 | Semestre 1 | 6 | ||||||||
| Analyse causale | 2 | Semestre 1 | 12 | ||||||||
| Méthodes pour les études de data mining et les études prédictives - Apprentissage automatique | 4 | Semestre 1 | 30 | ||||||||
| Programmation en Python | 3 | Semestre 1 | 21 | ||||||||
| Programmation en R | 3 | Semestre 1 | 21 | ||||||||
| Séminaires | 1 | Semestre 1 | 6 | ||||||||
| Statistique mathématique - Statistiques multivariées | 5 | Semestre 1 | 45 | ||||||||
| UE Optionelle : 1 seule au choix | |||||||||||
| Bases en statistiques et en génétique | 2 | Semestre 1 | 18 | ||||||||
| UE libre pour santé publique - 2 ECTS | 2 | Annualisé | |||||||||
Teaching Location(s)
Training campus
