ECTS
120 credits
Duration
2 years
Training Structure
School of Medicine, School of Pharmacy
Overview
The Master’s program in Epidemiology, Health Data, and Biostatistics (EDSB) trains specialists capable of designing studies, analyzing complex data, and interpreting results in the fields of health and life sciences. The program is based on three major complementary fields: epidemiology, biostatistics, and data science. It aims to provide students with dual expertise that combines their prior experience in understanding biological, medical, or public health issues with mastery of the quantitative methods necessary for the rigorous analysis of data. Our students are valuable assets to teams working in biology and health, as they possess the necessary background to grasp the issues at hand and the skills to develop data collection protocols, as well as to analyze and interpret this data appropriately and independently. This proper analysis of data in biology and health is a major challenge for research in the coming years, as data sets are becoming increasingly large and numerous, and errors in their analysis can lead (and have already led in the past) to erroneous or non-reproducible conclusions that undermine the credibility of the entire research field. Genuine expertise in data analysis is therefore essential today to address complex biological and/or health-related questions (biomarker discovery, identification of disease determinants, evaluation of the effectiveness of health interventions, personalized medicine, omics data, etc.). This objective is the “DNA” of our program.
The master’s program is designed in particular for students from the fields of health, life sciences, and health sciences who wish to develop expertise in data analysis applied to research. The first year of the master’s program is common to both tracks and provides a solid methodological foundation in epidemiology, statistics, programming, and data management and analysis. It then leads to two main tracks in the second year:
- the Health Data Analysis track, focused on clinical research, epidemiology, public health, and the use of health data collected in real-world settings (Real-World Data (RWD); Real-World Evidence (RWE));
- The Data Scientist track for the life sciences, focused on the analysis of complex biological data and data science methods applied to the life sciences.
This program allows students to gradually develop skills ranging from study design to statistical analysis, modeling, validation, interpretation, and the scientific application of results. The courses focus in particular on R, SAS, and Python, as well as modeling, machine learning, and artificial intelligence methods.
The master’s program prepares students for careers in academic or hospital research, healthcare facilities, public health agencies, the life sciences industry, and life sciences companies, as well as for pursuing a Ph.D.
Program
Select a program
Master's 1 in Epidemiology, Health Data, and Biostatistics (EDSB)
The EDSB Master 1 program serves as the common foundation for training in epidemiology, biostatistics, and data analysis as applied to health and the life sciences. It aims to provide students with the necessary foundations to understand, organize, and analyze data from a variety of contexts, including clinical research, epidemiological studies, public health, biology, and the life sciences.
The curriculum covers, among other topics, inferential statistics, the general linear model, quantitative epidemiology, randomized clinical trials, data mining, databases, and the analysis of omics data. The program also includes comprehensive hands-on training in the main programming languages used in data analysis—including R, SAS, and Python—as well as an introduction to big data and artificial intelligence in healthcare.
The goal is to train students who are not only able to apply statistical methods, but above all capable of answering a scientific question, because they will have understood the underlying assumptions, learned to select the appropriate methods for analyzing the data, and learned to correctly interpret the results and critically evaluate the analyses performed.
The M1 program thus provides a common methodological foundation before students specialize in the M2 program in health data analysis or data science applied to the life sciences. A two-month introductory research internship allows students to apply the knowledge they have acquired to a real-world research problem.
English
5 creditsCHOICE S1
2.5 creditsChoose one of the following 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 OMICS DATA
2.5 creditsBIG DATA & ARTIFICIAL INTELLIGENCE IN HEALTHCARE
2.5 creditsBASIC METHODS IN QUANTITATIVE EPIDEMIOLOGY
2.5 creditsCASE STUDY (Part 1)
2.5 creditsRANDOMIZED CLINICAL TRIALS
2.5 creditsPROJECT ENGINEERING & COMMUNICATION
2.5 credits
Master's 2 in Epidemiology, Health Data, and Biostatistics, with a concentration in Health Data
The EDSB Master 2 – Health Data Analysis (ADS) program provides training in the analysis and interpretation of data from clinical research, epidemiology, and public health, with a strong emphasis on real-world data (RWD) and its use to generate real-world evidence (RWE). It is designed for students in the EDSB Master’s 1 program who wish to develop methodological expertise in health research, as well as for physicians, pharmacists, and other healthcare professionals seeking to gain true autonomy in the design and analysis of their work.
The program aims to provide students with mastery of the entire methodological process: formulating the research question, selecting the study design, organizing and structuring data, defining the analysis plan, performing statistical analysis, interpreting results, and discussing their limitations. It combines a solid foundation in classical biostatistics—generalized and mixed models, survival analysis, and machine learning—with in-depth training in epidemiology and clinical research: methods in advanced quantitative epidemiology, biostatistics applied to diagnosis and prognosis, meta-analysis, and biostatistics for clinical research.
The course also covers current methods of health data analysis: new clinical research designs, observational and administrative health data, simulated trials, synthetic arms, synthetic data, health data warehouses (HDWs), SNDS, data standards, and generative artificial intelligence applied to health data. Significant emphasis is placed on use cases, data manipulation, programming, and the judicious use of AI for analysis.
The second semester is devoted to a research internship lasting at least four months, culminating in the writing of a thesis in the form of a scientific article and its defense.
This track enables health sciences students to pursue a career in university hospitals. Students from other fields can pursue positions as research engineers, biostatisticians, or methodologists in public health, research, and expert organizations; in research units working with health data; or in methodological support units for clinical research at healthcare institutions. Pursuing a Ph.D. also opens the door to a career as a researcher in epidemiology, biostatistics, or health data science.
Master's 2 in Epidemiology, Health Data, and Biostatistics (EDSB) with a specialization in Data Analysis for the Life Sciences
The EDSB Master 2 – Data Scientist for the Life Sciences program trains data scientists capable of working with a wide range of data from biology, health, and the life sciences more broadly. It offers a broad-based education tailored to the diversity of data and contexts encountered in both research and industry: experimental, industrial, pharmacological, and omics data, as well as data from complex measurement devices.
The track places a strong emphasis on machine learning, with a progression ranging from basic learning methods to deep learning. It shares a common foundation with the Health Data Analysis track (generalized and mixed linear models, analysis of censored data), which ensures a solid grounding in biostatistics. It expands on this foundation to address issues specific to industry and the life sciences: statistics for industry, data collection planning, advanced databases, and pharmacokinetic modeling combining mixed models and machine learning. The courses utilize R and Python.
The program relies heavily on project-based learning. Case studies and projects conducted in collaboration with other programs place students in situations that closely resemble real-world professional settings: teamwork, dialogue with specialists from other disciplines, and managing an end-to-end analysis project. Research seminars round out this interdisciplinary approach.
The program offers an apprenticeship track, which allows students to gain significant professional experience starting in the second year of the master’s program. Other students complete a six-month internship in a research laboratory or at a company. This program prepares students for careers as data scientists, biostatisticians, or research engineers in academic or hospital research laboratories, the pharmaceutical industry, the agri-food sector, or the biotechnology industry, as well as for pursuing a Ph.D.
ANALYSIS OF CENSORED DATA
2.5 creditsTIME SERIES STATISTICS
2.5 creditsGENERALIZED AND MIXED LINEAR MODEL
5 creditsBlended Model & Machine Learning Application
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