The Master 2, Data Science: Health, Insurance, Finance is a master's degree designed to train students from mathematics and applied mathematics backgrounds in data science (mathematical and computer science aspects) and advanced statistics.
Year organized into 5 blocks that can be compensated for each other, except for the internship
Information
Skills
- Understand, implement and develop statistical methods (mathematical, digital, statistical learning) for data science, deep learning and high dimensionality * Manage large databases * Select machine learning and statistical algorithms adapted to high dimensionality and relevant to application fields * Develop the computer programmes needed to implement them (python, R, spark) * Share the results obtained with appropriate visualisation tools.
Objectives
+ Train students from mathematics and applied mathematics programs in
data science (mathematical and computer science aspects) and
advanced statistics.
+ Understand and know how to use machine learning and deep learning algorithms
with a focus on three
areas of application (health, insurance, finance).
+ Enable students to acquire in-depth knowledge
of complex signals (data) from these
fields
Calendar
Capacity
Available Places
Target Audience and Entry Requirements
2nd year Master's Data Science: Health, Insurance, Finance is aimed in particular at 1st year Master's students in mathematics (or equivalent engineering school curriculum), and includes courses in inferential statistics and linear models, probability and stochastic processes, and programming (R and Python).
Application Period(s)
From 05/01/2026 to 10/07/2026
Supporting documents
Compulsory supporting documents
Letter of recommendation or internship evaluation.
Detailed description and hourly volume of courses taken since the beginning of the university program.
Additional supporting documents
Document indicating the list of local M2 choices available here : https://urlz.fr/i3Lo.
| Subjects | ECTS | Semester | Lecture | directed study | practical class | Lecture/directed study | Lecture/practical class | directed study/practical class | distance-learning course | Project | Supervised studies |
|---|---|---|---|---|---|---|---|---|---|---|---|
| English semester 2 | 2 | Semester 2 | 10 | ||||||||
| English Semester 1 | 2 | Semester 1 | 10 | ||||||||
| Subjects | ECTS | Semester | Lecture | directed study | practical class | Lecture/directed study | Lecture/practical class | directed study/practical class | distance-learning course | Project | Supervised studies |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Statistics | 6 | Semester 1 | 27 | 27 | |||||||
| Advanced Statistics and Machine Learning | 6 | Semester 2 | 24 | 27 | |||||||
| Deep Learning | 4 | Semester 2 | 15 | 15 | |||||||
| Bayesian statistic and discrete latent variables | 2.5 | Semester 1 | 12 | 15 | |||||||
| Machine Learning/ IA fiable | 2.5 | Semester 1 | 10.5 | 10.5 | |||||||
| Dimension reduction (FA+FI) | 2.5 | Semester 1 | 9 | 9 | |||||||
| Subjects | ECTS | Semester | Lecture | directed study | practical class | Lecture/directed study | Lecture/practical class | directed study/practical class | distance-learning course | Project | Supervised studies |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Numerical finance and models calibration | 6 | Semester 1 | 42 | ||||||||
| Financial econometrics | 3 | Semester 1 | 15 | 10 | |||||||
| Statistics for genetics and genomics (Health option) | 2.5 | Semester 2 | 9 | 9 | |||||||
| Introduction to genetics (health option) | 2.5 | Semester 1 | 9 | 9 | |||||||
| Survival and longitudinal data analysis (options health and insurance) | 5 | Semester 1 | 10.5 | 10.5 | |||||||
| Bioinformatics (Health option) | 2.5 | Semester 2 | 9 | 9 | |||||||
| Machine Learning for insurance and finance (option insurance and finance) | 5 | Semester 2 | 18 | 18 | |||||||
| 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 | 15 | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Basic of informatics | 2 | Semester 1 | 9 | 9 | |||||||
| Advanced Informatics | 4 | Semester 2 | 13.5 | 22.5 | |||||||
| Data Camp | 4 | Semester 2 | 9 | ||||||||
Teaching Location(s)
Training campus