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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
The most recent pitch for the Applied Data Science Profile for GSNS students can be found HEREThe official GSNS profile info page is here.

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. 

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 the elective courses table listed under B2. The illustration above visualises the Master’s profile Applied Data Science.
  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)
  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.

Eligible elective courses

Per Master's programme the elective options may differ. Ask your own master programme coordinator whether an elective course from a different programme is eligible for your studyplan as well.
Master's programmeElective courseOSIRIS 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  
Game and Media Technology Multimedia Retrieval INFOMR 
Game and Media Technology Pattern Recognition INFOMPR 
Mathematical Sciences Complex Networks  
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  
Showing 20 items from page electives-gsns sorted by Master's programme, Elective course. View more »

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