Health

Master 2 Epidemiology, Health Data, Biostatistics (EDSB) under Data Analyst for Life Sciences course

  • ECTS

    60 credits

  • Duration

    1 year

  • Training structure

    Faculty of Pharmacy

  • Language(s) of instruction

    French

Presentation

The proposed course is an evolution of the course "Statistics for Life Sciences" of the mention "Mathematics" of the previous period. The Master 1 becomes common with the course "Epidemiology, health data, biostatistics - Health data" with which the mutualization was already very important during the previous period.

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Objectives

Its objective remains to provide students coming mainly from health and biology bachelor's degrees to acquire a double competence in biostatistics.

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Know-how and skills

This dual competence is particularly sought after on the job market, as shown by the insertion rate figures at the end of the program. Our students are real assets in a team since they have the necessary culture in biology/health to master the problem of interest and the competence to analyze the data adequately. This adequate analysis of data in biology/health is a major issue for research in the years to come because the data are increasingly voluminous and numerous and errors in their analysis can lead (and have already led in the past) to erroneous or non-reproducible conclusions that discredit the entire research field. A real expertise in data analysis is therefore essential today to answer complex biological questions. This objective is the "DNA" of our training and will continue for the next period.

In addition, we have developed the content of the training to enable health and biology students to acquire skills that are ever closer to the job market in biostatistics: introduction of the Python language, reinforcement of lessons in Machine Learning and artificial intelligence. This evolution is also consistent with the change of mention because the health applications of these methods are increasingly numerous (biomarker research, personalized medicine, etc.). We have strengthened the teaching on this point, allowing our students (especially those who do not come from health fields) to become aware of the problems inherent in health data.

From a professional point of view, we are proposing a new case study course, allowing us to introduce project-based learning, the pedagogical virtues of which we know. This course is accompanied by an awareness of project management, preparing our students for their future jobs, which often involve working in teams built around projects.

 

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Organization

Knowledge control

Program

  • ANALYSIS OF CENSORED DATA

    2.5 credits
  • TIME STATISTICS

    2.5 credits
  • GENERALIZED AND MIXED LINEAR MODEL

    5 credits
  • Mixed model & machine learning application

    2.5 credits
  • CASE STUDY PART 2

    5 credits
  • MACHINE LEARNING LEVEL 1: APPLICATION TO PROGNOSIS

    2.5 credits
  • DATABASE LEVEL 2

    2.5 credits
  • MACHINE LEARNING LEVEL 2

    2.5 credits
  • PLANNED DATA COLLECTION

    2.5 credits
    • CC data plan collection

    • CT data plan collection

  • STATISTICS FOR INDUSTRY

    2.5 credits
  • RESEARCH SEMINARS

    2.5 credits
  • Master 2 internship

    25 credits
    • Internship thesis

    • Oral internship

  • M2 EDSB internship

    30 credits