Obligatory courses (22.5 EC)

There are three 7.5 EC obligatory courses: Data Science & Society, Computational thinking, and Data Analytics & Visualisation. If you already followed comparable courses (e.g. by doing the Master’s profile ‘Applied Data Science’ of the Graduate School of Life Sciences), you can, after consultation with the Programme Coordinator, follow a customised programme, composed of one or more electives (cf. below).

Elective courses (22.5 EC)

The ‘electives’ allow you to deepen your knowledge and skills in the use of health data to study health and diseases, in preparation of your research project. In consultation with the Programme Coordinator you can select courses at Utrecht University or at other institutions within or outside the Netherlands. These courses will provide you with a solid background in the application of data science within health, tailored to your specific needs and prior knowledge.

Should you have a deficiency in the general knowledge about health and diseases or about the biomedical sciences, then you will have to choose some specific electives (to an extent of at least 7.5 EC) that will compensate for this deficiency (such as ‘Clinical Epidemiology’, 'Study Design for Etiologic Research' or ‘Prognostic Research’).

Research project (45 EC)

The major research project of 45 EC focuses on the application of data science within health. In principle, students participate in on-going research within one of the affiliated research groups at UU or UMC Utrecht to experience the full-research cycle.

In consultation with the coordinator, the student may carry out this project within his own company or institution, under the condition that a researcher from the Utrecht University / UMC Utrecht can be found to guide the process and guarantee the required academic level.

Obligatory courses

Data Science & Society (compulsory)

This introductory course primarily aims to inspire and introduce you to the emerging domain of Applied Data Science. On the one hand it aims to trigger your enthousiasm for applied data science, and to inspire you to aim for societal impact through data science. On the other hand, it provides you with a core set of information science essentials to properly understand big data technologies, while leveraging the students’ diversity as an opportunity to create an inspiring course. Course assignments aim to explore data science and its societal impact, to survey the data science market landscape, to study selected scientific literature, and to practice with big data tools.

Computational thinking (compulsory)

This course starts with an introduction to proposition logic and basic algorithmics: it teaches you how to think like a computer. Next, you can exercise this thinking in building programmes in Python, a very commonly used programming language for working with large data sets.

Data Analytics & Visualisation (compulsory)

This course covers the main statistical methods used in data analysis and how to use most common machine learning algorithms and visualisation techniques. You will actively apply these methods in analysing real data sets and work together in interactive lecture sessions.