ECTS
60 credits
Duration
1 year
Training structure
School of Medicine, School of Pharmacy
Presentation
The proposed program is an evolution of the "Statistics for Health Sciences" program in the "Mathematics" track from the previous period. The Master 1 program will now be shared with the "Epidemiology, Health Data, Biostatistics – Data Analyst for Life Sciences" program, with which there was already significant overlap during the previous period.
Its objective remains to provide students, mainly from health and biology degree programs, with dual expertise in biostatistics. This dual expertise is particularly sought after in the job market, as shown by the employment rate figures upon completion of the program. Our students are real assets to any team, as they have the necessary background in biology/health to understand the issues at stake and the skills to analyze data appropriately. Proper analysis of biology/health data is a major challenge for research in the coming years, as data is becoming increasingly voluminous and numerous, and errors in its analysis can lead (and have already led in the past) to erroneous or non-reproducible conclusions that discredit the entire research sector. Real expertise in data analysis is therefore essential today in order to answer complex biological questions. This objective is the "DNA" of our training program and will continue to be so in the coming period.
In addition, the primary objective of changing the name of the program is to reflect the desired background of our students: the name "Mathematics" was misleading, since we only recruit students from health and biology programs in order to offer them dual expertise in biostatistics. However, these students do not naturally seek a master's degree in mathematics. Our visibility was therefore compromised by this affiliation.
In terms of development, it meets a need for the target audience, which will consist of health students and students changing direction who have completed the Specific Health Access Program (PASS) and the Health Access License (LAS), currently being implemented as part of the PACES reform.
In addition, we have updated the course content to enable health and biology students to acquire skills that are increasingly relevant to the biostatistics job market: introduction to the Python language, reinforcement of teaching in machine learning and artificial intelligence. This change is also consistent with the change in specialization, as the health applications of these methods are becoming increasingly numerous (biomarker research, personalized medicine, etc.).
Program
Select a program
Master's Degree in Epidemiology, Health Data, Biostatistics (EDSB)
The proposed program is an evolution of the "Statistics for Health Sciences" program in the "Mathematics" track from the previous period. The Master 1 program will now be shared with the "Epidemiology, Health Data, Biostatistics – Data Analyst for Life Sciences" program, with which there was already significant overlap during the previous period.
Its objective remains to provide students, mainly from health and biology degree programs, with dual expertise in biostatistics. This dual expertise is particularly sought after in the job market, as shown by the employment rate figures upon completion of the program. Our students are real assets to any team, as they have the necessary background in biology/health to understand the issues at stake and the skills to analyze data appropriately. Proper analysis of biology/health data is a major challenge for research in the coming years, as data is becoming increasingly voluminous and numerous, and errors in its analysis can lead (and have already led in the past) to erroneous or non-reproducible conclusions that discredit the entire research sector. Real expertise in data analysis is therefore essential today in order to answer complex biological questions. This objective is the "DNA" of our training program and will continue to be so in the coming period.
In addition, the primary objective of changing the name of the program is to reflect the desired background of our students: the name "Mathematics" was misleading, since we only recruit students from health and biology programs in order to offer them dual expertise in biostatistics. However, these students do not naturally seek a master's degree in mathematics. Our visibility was therefore compromised by this affiliation.
In terms of development, it meets a need for the target audience, which will consist of health students and students changing direction who have completed the Specific Health Access Program (PASS) and the Health Access License (LAS), currently being implemented as part of the PACES reform.
In addition, we have updated the course content to enable health and biology students to acquire skills that are increasingly relevant to the biostatistics job market: introduction to the Python language, reinforcement of teaching in machine learning and artificial intelligence. This change is also consistent with the change in specialization, as the health applications of these methods are becoming increasingly numerous (biomarker research, personalized medicine, etc.).
English
5 creditsCHOICE S1
2.5 creditsChoose one of two options:
TECHNOLOGICAL ASPECTS OF OMICS DATA COLLECTION
2.5 creditsMAJOR HEALTH ISSUES
2.5 credits
INTRODUCTION TO INFERENTIAL STATISTICS LEVEL 1
2.5 creditsR-SAS LEVEL 1
2.5 creditsINTRODUCTION TO INFERENTIAL STATISTICS LEVEL 2
2.5 creditsDATA MINING
5 creditsINTRODUCTION EPIDEMIOLOGY CLINICAL RESEARCH
2.5 creditsGENERAL MATHEMATICS
2.5 creditsLEVEL 1 DATABASE
2.5 creditsGENERAL LINEAR MODEL
2.5 credits
R-SAS LEVEL 2
2.5 creditsINTERNSHIP
10 creditsPYTHON
2.5 creditsSTATISTICAL ANALYSIS OF OMIC 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's degree
The proposed program is an evolution of the "Statistics for Health Sciences" program in the "Mathematics" track from the previous period. The Master 1 program will now be shared with the "Epidemiology, Health Data, Biostatistics – Data Analyst for Life Sciences" program, with which there was already significant overlap during the previous period.
Its objective remains to provide students, mainly from health and biology degree programs, with dual expertise in biostatistics. This dual expertise is particularly sought after in the job market, as shown by the employment rate figures upon completion of the program. Our students are real assets to any team, as they have the necessary background in biology/health to understand the issues at stake and the skills to analyze data appropriately. Proper analysis of biology/health data is a major challenge for research in the coming years, as data is becoming increasingly voluminous and numerous, and errors in its analysis can lead (and have already led in the past) to erroneous or non-reproducible conclusions that discredit the entire research sector. Real expertise in data analysis is therefore essential today in order to answer complex biological questions. This objective is the "DNA" of our training program and will continue to be so in the coming period.
In addition, the primary objective of changing the name of the program is to reflect the desired background of our students: the name "Mathematics" was misleading, since we only recruit students from health and biology programs in order to offer them dual expertise in biostatistics. However, these students do not naturally seek a master's degree in mathematics. Our visibility was therefore compromised by this affiliation.
In terms of development, it meets a need for the target audience, which will consist of health students and students changing direction who have completed the Specific Health Access Program (PASS) and the Health Access License (LAS), currently being implemented as part of the PACES reform.
In addition, we have updated the course content to enable health and biology students to acquire skills that are increasingly relevant to the biostatistics job market: introduction to the Python language, reinforcement of teaching in machine learning and artificial intelligence. This change is also consistent with the change in specialization, as the health applications of these methods are becoming increasingly numerous (biomarker research, personalized medicine, etc.).
Master's Degree in Epidemiology, Health Data, Biostatistics (EDSB) with a specialization in Data Analysis for Life Sciences
The proposed course is an evolution of the "Statistics for Life Sciences" course in the "Mathematics" program from the previous period. The Master 1 program will now be shared with the "Epidemiology, Health Data, Biostatistics - Health Data" course, with which there was already significant overlap during the previous period.
ANALYSIS OF CENSORED DATA
2.5 creditsTEMPORAL STATISTICS
2.5 creditsGENERALIZED LINEAR AND MIXED MODEL
5 creditsMixed model application & machine learning
2.5 creditsCASE STUDY PART 2
5 creditsMACHINE LEARNING LEVEL 1: APPLICATION TO FORECASTING
2.5 creditsLEVEL 2 DATABASES
2.5 creditsMACHINE LEARNING LEVEL 2
2.5 creditsPLANNED DATA COLLECTION
2.5 creditsINDUSTRY STATISTICS
2.5 creditsRESEARCH SEMINARS
2.5 credits
Master's degree internship
25 creditsM2 EDSB internship
30 credits