This Master’s programme provides for a thorough basis in and subsequently focuses on the use of health data to study health and diseases. Areas of interest include the identification of diseased individuals (diagnostic research), the understanding of causes of disease (etiologic research), the prediction of the natural course of disease (prognostic research), and the assessment of treatment effects (therapeutic research). In addition, focus will be on the use of health data to identify individuals at high risk of developing diseases (or the identification of those expected to stay healthy) and to assess the effects of preventive interventions. For that purpose, epidemiological and bio-statistical models will be complemented with an understanding of machine learning algorithms and artificial intelligence, in order to fully make use of the richness of health data.

Students will learn about different types of health data, how to retrieve information from various sources and how this information can be turned into a valuable resource for health research. Notably, they will learn about the advantages and disadvantages of different designs and analytical approaches to different data sources and for the different research purposes (diagnostic, etiologic, prognostic, therapeutic and prevention research). Methods range from multivariable prediction models of continuously updated measurements to genome-wide association studies. And from predicting individualised treatment effects to developing algorithms based on home-based wearables for early identification of disease occurrence or disease progression.

The curriculum covers 90 EC that can – if required – be spread over a period of three to five years. The design is set up in order to, on one hand, optimally prepare students for the exit qualifications of the degree and, on the other, to provide maximum flexibility for a personal approach to the degree (‘Be your own Master’).

Students can plan their own studies, generally over a period of three to five years, in consultation with the Programme Coordinator. All courses are given at least once a year. If required the Master's can be done full-time within a period of 18 months (cf. the Master's Epidemiology Postgraduate).

The Master’s Applied Data Science Postgraduate consists of the following main parts:

  1. Obligatory courses (22.5 EC)
  2. Elective courses (22.5 EC)
  3. Research project (45 EC)

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