M2 Data Science : Health, Insurance and Finance

Apply for the degree
  • Places available
    15
  • Language(s) of instruction
    English
    French
Présentation
Objectives

Train students from the fields of mathematics and applied mathematics – with knowledge in random mathematics (probability, statistics) and programming – in data science (mathematical and computational aspects) and in advanced statistics, placing particular emphasis on three fields of application (health, insurance, finance).

Location
EVRY
Course Prerequisites

Master 1 in mathematics (or equivalent qualification, such as engineering school training) that includes units in inferential statistics and linear modeling, probabilities and stochastic processes, programming (R and Python)

Additional information

Presentation of M2 Data Science : santé, assurance, finance on video.

Skills

  • Understand and proficiently use high-level mathematical tools and methods.

  • Understand and mathematically model a problem in order to resolve it.

  • Be proficient in the use of digital tools and major programming languages.

  • Analyse data and implement digital simulations.

  • Be able to manage a project.

Post-graduate profile

Students leave this programme with a data science engineer profile. They are trained in statistics, statistical learning and informatics. They can therefore apply for jobs such as Data Scientist / Data analyst / Statistician. The programme has a solid theoretical content which also prepares graduates for doctoral studies.

Career prospects

Students are trained to exercise "data scientist" and "statistician" professions, particularly in health, insurance and finance companies, but not exclusively. We have maintained a significant number of ECTS credits on fundamental subjects, thus enabling students to further their studies at doctoral level.

Collaboration(s)
Laboratories

Laboratoire de Mathématiques et Modélisation d'Evry.

Programme

L'ensemble des volumes horaires des cours dispensés est identique pour les étudiants inscrits en formation initiale et en apprentissage à l'exclusion du cours de stastistics (FI 78h    ; FA54h).

Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
English S3 2 18
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Informatics 6 4.5 34.5
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Statistics 6 27 27
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Machine learning 9 39 42
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Option I assurance 5 18 18
Option I finance 5 21 21
Options I Santé 5 18 18

L'ensemble des volumes horaires des cours dispensés est identique pour les étudiants inscrits en formation initiale et en apprentissage à l'exclusion du cours de stastistics (FI 78h    ; FA54h).

Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
English S4 2 18
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Advanced Statistics and Machine Learning 6 27 27
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Option II assurance 5 27 27
Option II finance 5 27 27
Options II sante 5 27 27
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Data Camp 4 10 5
Subjects ECTS Lecture directed study practical class Lecture/directed study Lecture/practical class directed study/practical class distance-learning course Project Supervised studies
Stage 15 10
Modalités de candidatures
Application period
From 15/04/2024 to 09/07/2024
Compulsory supporting documents

  • Motivation letter.

  • All transcripts of the years / semesters validated since the high school diploma at the date of application.

  • Curriculum Vitae.

  • Detailed description and hourly volume of courses taken since the beginning of the university program.

Additional supporting documents

  • VAP file (obligatory for all persons requesting a valuation of the assets to enter the diploma).

  • The application procedure, which depends on your nationality and your situation is explained here : https://urlz.fr/i3Lo.

  • 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.

Contact(s)
Course manager(s)
Christophe Ambroise - christophe.ambroise@univ-evry.fr
Marie-Luce Taupin - marie-luce.taupin@univ-evry.fr
Administrative office
Laura Djimtangar - laura.djimtangar@univ-evry.fr