Health

Epidemiology, Health Data, Biostatistics - EDSB

  • ECTS

    60 credits

  • Duration

    1 year

  • Training structure

    Faculty of Medicine, Faculty of Pharmacy

Presentation

The proposed course is an evolution of the "Statistics for Health Sciences" course in the "Mathematics" field from the previous period. Master 1 is now shared with the "Epidemiology, Health Data, Biostatistics - Data Analyst for Life Sciences" course, with which there was already a high degree of mutualization during the previous period. 

Its objective remains to provide students, mainly from the Health curriculum and biology bachelor's degrees, with the opportunity to acquire dual skills in biostatistics. This dual competence is particularly sought-after on the job market, as shown by the insertion rate figures at the end of the course. Our students are real assets to a team, as they have the necessary biology/health culture to master the problem of interest, and the skills to analyze the data appropriately. Proper data analysis in biology/health is a major challenge for research in the years to come, as data are becoming ever more voluminous and numerous, and errors in their analysis can lead (and have already led in the past) to erroneous or non-reproducible conclusions, discrediting the entire research sector. Genuine expertise in data analysis is therefore essential to answer complex biological questions. This objective is the "DNA" of our training program, and will remain so for the coming period.

In addition, the first objective of the change of major is to be consistent with the desired origin of our students: the situation in the "Mathematics" major was misleading, as we will only be recruiting students from the health and biology streams to offer them a dual competence in biostatistics. However, these students will not naturally seek their Master's degree in a "Mathematics" field. Our readability was therefore compromised by this attachment.

In terms of development, it meets a need in terms of the target public, which will be made up of health students and students reorienting from the Parcours d'Accès Spécifique Santé (PASS) and the Licence Accès Santé (LAS), currently being set up as part of the PACES reform.

In addition, we have developed the course content to enable students in the health and biology fields to acquire skills in biostatistics that are increasingly relevant to the job market: introduction of the Python language, reinforcement of teaching in Machine Learning and artificial intelligence. This development is also in line with the change of mention, as the healthcare applications of these methods are increasingly numerous (biomarker research, personalized medicine, etc.).

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Program

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Master 1 Epidemiology, Health Data, Biostatistics (EDSB)

The proposed course is an evolution of the "Statistics for Health Sciences" course in the "Mathematics" field from the previous period. Master 1 is now shared with the "Epidemiology, Health Data, Biostatistics - Data Analyst for Life Sciences" course, with which there was already a high degree of mutualization during the previous period. 

Its objective remains to provide students, mainly from the Health curriculum and biology bachelor's degrees, with the opportunity to acquire dual skills in biostatistics. This dual competence is particularly sought-after on the job market, as shown by the insertion rate figures at the end of the course. Our students are real assets to a team, as they have the necessary biology/health culture to master the problem of interest, and the skills to analyze the data appropriately. Proper data analysis in biology/health is a major challenge for research in the years to come, as data are becoming ever more voluminous and numerous, and errors in their analysis can lead (and have already led in the past) to erroneous or non-reproducible conclusions, discrediting the entire research sector. Genuine expertise in data analysis is therefore essential to answer complex biological questions. This objective is the "DNA" of our training program, and will remain so for the coming period.

In addition, the first objective of the change of major is to be consistent with the desired origin of our students: the situation in the "Mathematics" major was misleading, as we will only be recruiting students from the health and biology streams to offer them a dual competence in biostatistics. However, these students will not naturally seek their Master's degree in a "Mathematics" field. Our readability was therefore compromised by this attachment.

In terms of development, it meets a need in terms of the target public, which will be made up of health students and students reorienting from the Parcours d'Accès Spécifique Santé (PASS) and the Licence Accès Santé (LAS), currently being set up as part of the PACES reform.

In addition, we have developed the course content to enable students in the health and biology fields to acquire skills in biostatistics that are increasingly relevant to the job market: introduction of the Python language, reinforcement of teaching in Machine Learning and artificial intelligence. This development is also in line with the change of mention, as the healthcare applications of these methods are increasingly numerous (biomarker research, personalized medicine, etc.).

See the complete page of this course

  • English

    5 credits
  • CHOICE S1

    2.5 credits
    • Your choice: 1 of 2

      • TECHNOLOGICAL ASPECTS OF OMICS DATA COLLECTION

        2.5 credits
      • MAJOR HEALTH ISSUES

        2.5 credits
  • INTRODUCTION TO INFERENTIAL STATISTICS LEVEL 1

    2.5 credits
  • R-SAS LEVEL1

    2.5 credits
  • INTRODUCTION TO INFERENTIAL STATISTICS LEVEL 2

    2.5 credits
  • DATA MINING

    5 credits
    • Data Mining CT

    • Data Mining CC

  • INTRODUCTION EPIDEMIOLOGY CLINICAL RESEARCH

    2.5 credits
  • GENERAL MATHEMATICS

    2.5 credits
  • DATABASE LEVEL1

    2.5 credits
    • Databases Level 1 Written

    • Databases level 1 Project

  • GENERAL LINEAR MODEL

    2.5 credits
  • R-SAS LEVEL 2

    2.5 credits
    • Written R-SAS

    • R-SAS written reports

  • STAGE

    10 credits
  • PYTHON

    2.5 credits
  • STATISTICAL ANALYSIS OF OMICS DATA

    2.5 credits
  • BIG DATA & ARTIFICIAL INTELLIGENCE IN HEALTHCARE

    2.5 credits
  • METHODS IN QUANTITATIVE EPIDEMIOLOGY BASIC LEVEL

    2.5 credits
  • CASE STUDY (part 1)

    2.5 credits
  • RANDOMIZED CLINICAL TRIALS

    2.5 credits
  • PROJECT ENGINEERING & COMMUNICATION

    2.5 credits

Master 2

The proposed course is an evolution of the "Statistics for Health Sciences" course in the "Mathematics" field from the previous period. Master 1 is now shared with the "Epidemiology, Health Data, Biostatistics - Data Analyst for Life Sciences" course, with which there was already a high degree of mutualization during the previous period. 

Its objective remains to provide students, mainly from the Health curriculum and biology bachelor's degrees, with the opportunity to acquire dual skills in biostatistics. This dual competence is particularly sought-after on the job market, as shown by the insertion rate figures at the end of the course. Our students are real assets to a team, as they have the necessary biology/health culture to master the problem of interest, and the skills to analyze the data appropriately. Proper data analysis in biology/health is a major challenge for research in the years to come, as data are becoming ever more voluminous and numerous, and errors in their analysis can lead (and have already led in the past) to erroneous or non-reproducible conclusions, discrediting the entire research sector. Genuine expertise in data analysis is therefore essential to answer complex biological questions. This objective is the "DNA" of our training program, and will remain so for the coming period.

In addition, the first objective of the change of major is to be consistent with the desired origin of our students: the situation in the "Mathematics" major was misleading, as we will only be recruiting students from the health and biology streams to offer them a dual competence in biostatistics. However, these students will not naturally seek their Master's degree in a "Mathematics" field. Our readability was therefore compromised by this attachment.

In terms of development, it meets a need in terms of the target public, which will be made up of health students and students reorienting from the Parcours d'Accès Spécifique Santé (PASS) and the Licence Accès Santé (LAS), currently being set up as part of the PACES reform.

In addition, we have developed the course content to enable students in the health and biology fields to acquire skills in biostatistics that are increasingly relevant to the job market: introduction of the Python language, reinforcement of teaching in Machine Learning and artificial intelligence. This development is also in line with the change of mention, as the healthcare applications of these methods are increasingly numerous (biomarker research, personalized medicine, etc.).

See the complete page of this course

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

The proposed course is an evolution of the "Statistics for Life Sciences" course in the "Mathematics" field from the previous period. Master 1 is now shared with the "Epidemiology, Health Data, Biostatistics - Health Data" course, with which there was already a high degree of mutualization during the previous period.

See the complete page of this course

  • 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
    • Thesis internship

    • Oral internship

  • M2 EDSB internship

    30 credits