This course is an evolution of the "Statistics for Health Sciences" course of "Mathematics." Master 1 becomes familiar with the course "epidemiology, health data, biostatistics - Data Analyst for Life Sciences," with which the pooling was already essential during the previous period.
Its objective remains to provide students mainly from health and biology bachelor's degrees to acquire a dual biostatistics competence. This dual skill is particularly sought after on the job market, as shown by the figures for the integration rate at the end of the course. Our students are real assets in a team since they have the necessary culture in biology/health to master the issue of interest and the competence to analyse the data adequately. This adequate analysis of data in biology/health is a significant issue for research in the years to come because the data are increasingly voluminous and numerous, and errors in their analysis can lead (and has already led in the past) to erroneous or non-reproducible conclusions discrediting the entire research sector. Real expertise in data analysis is therefore essential today in order to answer complex biological questions. This goal is the "DNA" of our training and continues for the next period.
The first objective of changing the grade is to be consistent with our students' desired origin: the "Mathematics" grade situation was misleading since we will only recruit students from the health and biology fields to offer them dual competence in biostatistics. However, these students do not naturally seek their master's degree in "Mathematics." This connection, therefore, compromised our readability.
In terms of development, it corresponds to a need concerning the target audience, which will be made up of health students and reorientation students from the Specific Health Access Path (PASS) and the Health Access License (LAS) implemented as part of the reform of the PACES.
We have changed the training content to allow students in health and biology to acquire skills that are ever closer to the job market in biostatistics: introduction of the Python language, strengthening of machine learning lessons, and artificial intelligence. This development is also consistent with the change in designation because these methods' health applications are more and more numerous (research for biomarkers, personalized medicine, etc.).