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 of the "Mathematics" mention of the previous period. The Master 1 becomes common with the course "Epidemiology, Health Data, Biostatistics - Data Analyst for Life Sciences" with which there was already a high degree of mutualization during the previous period.
Its objective remains to provide students coming mainly from the Health curriculum and biology degrees with a dual competence in biostatistics. This double skill 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 in a team since they have the necessary culture in biology/health to master the problematic of interest and the competence to analyze the data in an adequate way. 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. 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, the first objective of the change of mention is to be consistent with the desired origin of our students: the situation in the "Mathematics" mention was misleading since we will only recruit students from the health and biology streams to offer them a double competence in biostatistics. However, these students will not naturally seek their master's degree in a "Mathematics" field. Our legibility was therefore compromised by this attachment.
In terms of evolution, it corresponds to a need concerning the target public which will be made up of health students and students in reorientation coming from the Specific Health Access Course (PASS) and the Health Access License (LAS), being set up within the framework of the reform of the PACES.
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.).
Program
Select a program
Master 1 Epidemiology, Health Data, Biostatistics (EDSB)
The proposed course is an evolution of the "Statistics for Health Sciences" course of the "Mathematics" mention of the previous period. The Master 1 becomes common with the course "Epidemiology, Health Data, Biostatistics - Data Analyst for Life Sciences" with which there was already a high degree of mutualization during the previous period.
Its objective remains to provide students coming mainly from the Health curriculum and biology degrees with a dual competence in biostatistics. This double skill 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 in a team since they have the necessary culture in biology/health to master the problematic of interest and the competence to analyze the data in an adequate way. 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. 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, the first objective of the change of mention is to be consistent with the desired origin of our students: the situation in the "Mathematics" mention was misleading since we will only recruit students from the health and biology streams to offer them a double competence in biostatistics. However, these students will not naturally seek their master's degree in a "Mathematics" field. Our legibility was therefore compromised by this attachment.
In terms of evolution, it corresponds to a need concerning the target public which will be made up of health students and students in reorientation coming from the Specific Health Access Course (PASS) and the Health Access License (LAS), being set up within the framework of the reform of the PACES.
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.).
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 of the "Mathematics" mention of the previous period. The Master 1 becomes common with the course "Epidemiology, Health Data, Biostatistics - Data Analyst for Life Sciences" with which there was already a high degree of mutualization during the previous period.
Its objective remains to provide students coming mainly from the Health curriculum and biology degrees with a dual competence in biostatistics. This double skill 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 in a team since they have the necessary culture in biology/health to master the problematic of interest and the competence to analyze the data in an adequate way. 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. 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, the first objective of the change of mention is to be consistent with the desired origin of our students: the situation in the "Mathematics" mention was misleading since we will only recruit students from the health and biology streams to offer them a double competence in biostatistics. However, these students will not naturally seek their master's degree in a "Mathematics" field. Our legibility was therefore compromised by this attachment.
In terms of evolution, it corresponds to a need concerning the target public which will be made up of health students and students in reorientation coming from the Specific Health Access Course (PASS) and the Health Access License (LAS), being set up within the framework of the reform of the PACES.
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.).
Master 2 Epidemiology, Health Data, Biostatistics (EDSB) under Data Analyst for Life Sciences course
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.
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