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 opportunities that these data potentially provide while avoiding the many pitfalls? The master’s profile Applied Data Science addresses this challenge.
Note: there are master’s programmes for which the overlap in content between the master and the ADS profile is too large. This implies that students from these masters cannot follow the profile. For the GSNS this holds for all Master’s programmes in Information and Computing Sciences.
Applied Data Science (ADS) 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. Two mandatory courses provide a thorough introduction to data science, its basic methods, techniques, processes, and the application of data science within specific domains. The foundations of applied data science include relevant statistical methods, machine learning techniques and programming skills.
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.
Upon completion of the Master’s profile Applied Data Science the student:
- Understands the basic methods and techniques in data science
- Is able to apply this knowledge and analyse large datasets in a specific domain