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

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

 

 

 

 

 

Curriculum

 

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)
    1. Data Science & Society (coordinator: dept. Computer science / GSNS; in period 1)
    2. Data Analysis & Visualisation (coordinator: dept. Methods & Statististics / GSSBS; in period 2)
    • NB: By registering for the ADS profile, you are automatically registered for the obligatory DS&S and DA&V courses, even without OSIRIS enrollment beforehand.
  2. Research project on an Applied Data Science topic (15 EC) OR Two elective courses (15 EC)
    1. 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.
    2. Two elective courses (15 EC). The elective courses list below is still incomplete. Please 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

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

Eligible elective courses

Master's programme

Elective course OSIRIS code and URL
Artificial Intelligence Cognitive Modeling  INFOMCM
Artificial Intelligence Experimentation in Psychology and Linguistics  INFOMEPL
Artificial Intelligence Logic and Computation WBMV13005
Artificial Intelligence Logic and Language  TLMV13020
Artificial Intelligence Multi-agent learning  INFOMAA
Business Informatics  Business intelligence  INFOMBIN
Business Informatics  Software Architecture INFOMSWA
Climate Physics  Measuring Analyzing and Interpreting Observations NS-MO501M
Computing Science  Big Data INFOMBD
Computing Science  Data mining INFOMDM
Computing Science  Pattern recognition INFOMPR
Computing Science  Pattern set mining INFOMPSM
Experimental Physics  Statistical Data Analysis NS-EX414M
Game and Media Technology  Multimedia Retrieval INFOMR
Game and Media Technology  Pattern Recognition INFOMPR
Mathematical Sciences  Network Dynamics WISL116
Mathematical Sciences  Parallel Algorithms WISL603
Mathematical Sciences  Seminar Scientific Computing  WISM470

Methodology and Statistics for the
Behavioural, Biomedical and Social Sciences 

Computational inference with R  201300004