Master in Public Health Data Science

General

Program Description

This international Master program provides a year of international research into public health data science, from project design to real-life health data analysis and the communication of results.

Program factsheet

  • Academic cooperation: The Master in Public Health Data Science is delivered along with the Bordeaux School of Public Health (ISPED) and the Bordeaux Population Health Research Center (BPH).
  • Admission requirements:
    • Candidates must fulfill the following: hold at least a Master (year 1) degree with honors (minimum 240 ECTS or equivalent) in one (or more) of the following disciplines: statistics, informatics or epidemiology.
  • Program duration: 1 year, including an internship (60 ECTS).
  • Language requirements: This program is taught entirely in English. Excellent proficiency in English is therefore required. Students whose native language is not English must provide a TOEFL, TOEIC or IELTS certification: TOEFL score of 550/213/80, TOEIC score of 900-990 or IELTS score of 6.0.
  • Level: Master (year 2) of Science in Public Health Data Science.
  • Tuition fees:
    • Annual registration fees for all selected applicants are calculated according to the rules and regulations of the University of Bordeaux.
    • Financial aid and housing grants may be awarded to selected applicants according to the criteria of excellence.

Program outline

The Master in Public Health Data Science provides a year of international research into public health data science, from project design to real-life health data analysis and the communication of results. Selected within the French “Investments for the Future” program as an “Initiative of Excellence”, the program covers multidisciplinary skills in epidemiology, informatics, and statistics, and ensures that students gain strong knowledge about the strengths and limits of digital technologies and their use in public health research.

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Program structure

Semester 3 to 4: Content

Semester 3

  • Basics: 6 ECTS

Focus on basic knowledge and the functional capabilities of the tools used in health data analytics.

  • Electronic health data (6 ECTS)

Focus on the skills required to conceptualize, manage, analyze and communicate via health research carried out by Electronic Health Records (HER) and medicoadministrative databases (MA-DBs).

  • Digital cohorts (6 ECTS)

Focus on the skills required to conceptualize, manage, analyze and communicate via cohort studies that integrate digital tools.

  • Web-based data (6 ECTS)

Focus on the abilities needed to prepare Public Health studies that integrate data from social networks and web forums, linked open data and mobile data. The practice is carried out via a dedicated case study that involves the processing of large mobile dataset (call details records).

  • Omics data (6 ECTS)

Focus on the abilities needed to conceptualize, manage, analyze and communicate using clinical studies that integrate high dimensional data.

Semester 4

  • Value creation (6 ECTS)

This final course prepares students so that as graduates, they are capable of becoming immediate contributors in the workplace whether it be in the academic or the industrial sector. Students learn to develop their entrepreneurial skills and also acquire an understanding of the societal and economic value created by digital public health data research.

  • Internship (24 ECTS)

Students may complete their internship either with the research team that generated a project case study during the Public Health Data Science Master program or else with a new team from the extensive research network of the Graduate Program.

Strengths of this Master's program?

Epidemiology

Translation of a public health/clinical problem into a research question, including the design of the research plan for surveillance systems, observational and experimental studies (i.e. clinical trials), the evaluation of validity and causality of an association.

Statistics

Methods for supervised and unsupervised statistical analysis and modeling of biomedical data (including high-dimensional and time-to-event data), statistical learning, data mining, data integration, advanced computational statistics.

Informatics

The architecture of data integration (i2b2, Transmart), interoperability, knowledge representation (terminologies, ontologies), natural language processing, data visualization, programming, cloud computing, and Hadoop, linked open data, security, confidentiality and integrity of data.

Last updated Oct 2019

About the School

The University of Bordeaux was newly established in 2014 following the merger of three universities. Ranked among the top universities in France for the quality of its academic courses and research, i ... Read More

The University of Bordeaux was newly established in 2014 following the merger of three universities. Ranked among the top universities in France for the quality of its academic courses and research, it was awarded the “Initiative of Excellence” by the French government in 2011. Read less
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