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.).
Program
Select a program
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.).
English
5 creditsCHOICE S1
2.5 creditsYour choice: 1 of 2
TECHNOLOGICAL ASPECTS OF OMICS DATA COLLECTION
2.5 creditsMAJOR HEALTH ISSUES
2.5 credits
INTRODUCTION TO INFERENTIAL STATISTICS LEVEL 1
2.5 creditsR-SAS LEVEL1
2.5 creditsINTRODUCTION TO INFERENTIAL STATISTICS LEVEL 2
2.5 creditsDATA MINING
5 creditsINTRODUCTION EPIDEMIOLOGY CLINICAL RESEARCH
2.5 creditsGENERAL MATHEMATICS
2.5 creditsDATABASE LEVEL1
2.5 creditsGENERAL LINEAR MODEL
2.5 credits
R-SAS LEVEL 2
2.5 creditsSTAGE
10 creditsPYTHON
2.5 creditsSTATISTICAL ANALYSIS OF OMICS DATA
2.5 creditsBIG DATA & ARTIFICIAL INTELLIGENCE IN HEALTHCARE
2.5 creditsMETHODS IN QUANTITATIVE EPIDEMIOLOGY BASIC LEVEL
2.5 creditsCASE STUDY (part 1)
2.5 creditsRANDOMIZED CLINICAL TRIALS
2.5 creditsPROJECT 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.).
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.
ANALYSIS OF CENSORED DATA
2.5 creditsTIME STATISTICS
2.5 creditsGENERALIZED AND MIXED LINEAR MODEL
5 creditsMixed model & machine learning application
2.5 creditsCASE STUDY PART 2
5 creditsMACHINE LEARNING LEVEL 1: APPLICATION TO PROGNOSIS
2.5 creditsDATABASE LEVEL 2
2.5 creditsMACHINE LEARNING LEVEL 2
2.5 creditsPLANNED DATA COLLECTION
2.5 creditsSTATISTICS FOR INDUSTRY
2.5 creditsRESEARCH SEMINARS
2.5 credits
Master 2 internship
25 creditsM2 EDSB internship
30 credits