M2 Data Science : Health, Insurance and Finance

Apply for the degree
  • Places available
    15
  • Language(s) of instruction
    Anglais
    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)

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    ; FA=54h).

Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
English S3 2 18
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Informatics 6 4.5 34.5
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Statistics 6 27 27
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Machine learning 9 39 42
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Options I Santé 5 18 18
Option I finance 5 21 21
Option I assurance 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    ; FA=54h).

Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
English S4 2 18
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Advanced Statistics and Machine Learning 6 27 27
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Options II sante 5 27 27
Option II finance 5 27 27
Option II assurance 5 27 27
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Data Camp 4 10 5
Matières ECTS Cours TD TP Cours-TD Cours-TP TD-TP A distance Projet Tutorat
Stage 15 10
Modalités de candidatures
Application period
From 15/04/2021 to 09/07/2021
Compulsory supporting documents
  • Curriculum EU (description of the units of education followed) of the last two years.

  • Motivation letter.

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

  • Curriculum Vitae.

Additional supporting documents
Contact(s)
Course manager(s)
Administrative office
Adelia Soarez Da Costa - adelia.soaresdacosta@univ-evry.fr