master 2, Data Science: Health, Insurance, Finance

Data science is currently experiencing a significant boom that disrupts its interface, particularly with statistics, and requires an interdisciplinary approach. This change is based on:

the growing importance of mathematical modeling (analysis, prediction, and integration of heterogeneous data)

access to massive data from new technologies (internet, business analytics, biotechnologies, DNA chips) requiring sophisticated statistical and computer processing. The use of statistics, machine learning, and IT for data science marks the start of a significant transformation that affects all sectors: from e-commerce to scientific research, including health, insurance, and finance!

Educational objective:

Solid generalist training in statistics and machine learning is intended for applied mathematicians and engineers wishing to complete their training and become specialists in data science.

Opening lessons in genetic epidemiology and insurance

Goal: to allow students to acquire an in-depth knowledge of complex signals (data) from these fields

Opportunities:

in business: biopharmaceutical, health or agri-food industry, banks, and insurance)

academic (higher education, CNRS, INRA, INRIA, INSERM, CEA, IRSTEA)

Offers the best conditions for:

to integrate professionally at the end of M2

to prepare for a doctorate in the mathematics/life sciences and mathematics/insurance and finance interfaces

Targeted positions:

data scientist / engineer-statistician / biostatistician/finance analyst for whom the demand is very high (large groups, start-ups, biotechs, CRO, IT services company).

Access to professions of applied mathematicians unrelated to life sciences or finance and insurance, connected with big data (internet, business analytics=), production optimization (resource management, pricing), e-marketing, modeling for product design.