Welcome to this landing page on the various Applied Data Science educational programmes at Utrecht University. We currently offer three flavors:

  1. Applied Data Science Profile for GSNS students: This master’s profile comprises two mandatory multidisciplinary courses (15 EC) complemented with either a research project (15 EC) or a selection of two elective courses (15 EC) from the list of pre-approved elective courses. See the section below for more information. Here is the most recent pitch.
  2. Applied Data Science Profile for GSLS students: This master’s profile comprises three mandatory multidisciplinary courses (22.5 EC) complemented with one applied data science-related elective course (7.5 EC). A portfolio assignment (3 EC) concludes the Master’s profile Applied Data Science. Here is the most recent pitch
Applied Data Science Profile (GSNS)

Data are everywhere. From the sciences to industry, commerce, and government, large collections of diverse data are becoming increasingly more indispensable for decision making, planning, and knowledge discovery. But how can we sensibly take advantage of all the opportunitities that these data potentially provide while avoiding the many pitfalls? The Master’s profile Applied Data Science addresses this challenge.

Applied Data Science is a multidisciplinary profile for students who are not only interested in broadening their knowledge and expertise within the field of Data Science, but are also eager to apply these capabilities in relevant projects within their research domain. The two mandatory courses provide a thorough introduction to data science, its basic methods, techniques, processes, and the application of data science within a specific domain. The foundations of applied data science include relevant statistical methods, machine learning techniques and programming. Moreover, key aspects and implications of ethics, privacy and law are covered as well.

The multidisciplinary nature of the Applied Data Science profile is also embodied in the collaborative design of the mandatory courses and (optionally) the research project. This means that both the teaching staff and students will have different backgrounds as means to help broaden perspectives and stimulate creativity. We investigate data science methods and techniques through case studies and applications throughout the life sciences & health, social sciences, geosciences, and the humanities. Therefore, students applying for this master’s profile should have an affinity for this multidisciplinary approach. You can register for the ADS profile by clicking the Register button below and then filling out the form.


Register for ADS profile GSNS

the Master's profile ADS

The master’s profile comprises 2 mandatory multidisciplinary courses (15 EC) complemented with either a research project (15 EC) OR a selection of 2 elective courses (15 EC) from a list of preapproved elective courses.

1. Two mandatory courses (15 EC)

Data Science & Society (coordinator: dept. Computer science/GSNS; in period 1)
Data Analysis & Visualisation (coordinator: dept. Methods & Statististics/GSSBS; in period 2)

Program specific requirements

The procedure for enrolling into the Data Analysis & Visualisation course has been formalised now due to many problems in previous years: 

a. As participants in the ADS profile, you can pre-register via this registration form. This pre-registration is now open, and will remain open until the 27 September deadline. Please note: this form is only intended for ADS profile participants!
b. All other students can register via Osiris. That is possible from 7 to 11 October. Registration is open until the course is full.
c. Profile students who miss the deadline will have to register via Osiris via October 7 to 11. They do not receive any special priority. Registration is no longer possible after 11 October.
d. In the time between September 27 and October 7 we will transfer the list that results from the pre-registration form to Osiris once (!) We do no manual registrations!

2. Research project on an Applied Data Science topic (15 EC) OR a selection of 2 elective courses (15 EC) from a list of preapproved elective courses

a. Research project on an Applied Data Science topic (15 EC): Focus should be on interdisciplinary aspects and at least two supervisors from different departments/faculties should be involved. The topic should not correspond to the topic of the master thesis, however if the master reseach project deals with an applied data science subject, it is for certain master’s programmes permitted to combine the research project of the master’s profile Applied Data Science (15 EC) with the master research thesis. Both parts must be separately assessed and a supervisor from a different department or faculty is involved in this part of the research project. The topic should be approved by a member of the Applied Data Science steering committee who is involved, and by the programme director of the master programme for which the student is admitted.

b. Two elective courses (15 EC). Please find the elective courses list below. You can ask your Master’s programme coordinator for up to date information.

Please note that the total number of EC of each master’s programme will NOT be increased by completing the master profile Applied Data Science. Students receive a certificate by completing the Master’s profile Applied Data Science.

Learning outcomes:

  • Understands the basic methods and techniques in data science
  • Is able to apply this knowledge and analyse large datasets in a specific domain
  • Understands the potential and risks of applying data science for research and society
  • Is able to work in interdisciplinary teams

Eligible elective courses

Master's programme

Elective course OSIRIS code
Artificial Intelligence Multi-agent learning INFOMAA
  Cognitive Modeling INFOMCM
  Experimentation in Psychology and Linguistics INFOMEPL
  Logic and Language  TLMV13020
  Logic and Computation WBMV13005
  Natural Language Processing INFOMNLP
Business Informatics  Process Mining INFOMPROM
Climate Physics  Simulation of Ocean, Atmosphere and Climate NS-MO501M
Computing Science  Data mining INFOMDM
  Pattern set mining INFOMPSM
  Big data INFOMBD
  Pattern recognition INFOMPR
  Data Intensive System INFOMDIS
Experimental Physics  Statistical Data Analysis NS-EX434M
Game and Media Technology  Multimedia Retrieval INFOMR
  Pattern Recognition INFOMPR
Human Computer Interaction Adaptive Interactive Systems INFOMAIS
Mathematical Sciences  Parallel Algorithms WISL603
  Statistics for Stochastic Processes WISL116
  Complex Networks  WISL115

Methodology and Statistics for the
Behavioural, Biomedical and Social Sciences 

Computational inference with R 201300004


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