Educational profile – teacher degree
If you are passionate about sharing your knowledge, and you would consider a career as a teacher in secondary education, this profile might be right for you. A profile is a coherent set of courses totaling 30 EC and can be chosen to expand the thematic range of your Master's programme. The courses are on a single theme that is usually not a standard part of the programme.
The emphasis of the Educational Profile is on practitioner skills and school-based activities. Throughout the profile, learning theories and teaching methods will be taught closely linked to your day to day work in the classroom. The profile is tailored to meet the professional development needs of teachers in the early stages of their careers. More information.
Students with deficiencies in the required entrance knowledge for the AI master programme can (subject to approval) use at most 15 EC to take courses (usually at the undergraduate level) to remedy those. For students with more deficiencies the admissions committee could, if feasible given the student's background and knowledge, offer the student a pre-master programme of 30 EC.
At the start of the programme, each student discusses his or her interests with the Artificial Intelligence student advisor.
Together, an individual study plan is drawn up that fits the background and interests of the student. The study plan details all courses the student intends to take; it may always be changed in a later phase after checking with the student advisor. During the first part of the study, the Artificial Intelligence student advisor will be the primary contact for any study-related questions and problems. During the final thesis project, this role is taken over by a staff member supervising the final project.
Applied Data Science profile
Applied Data Science is a multidisciplinary profile for students who want to better understand and be able to apply Data Science methods, techniques, and processes to solve real-world problems in various application domains. The foundations of applied data science include relevant statistical methods, machine learning techniques and script programming. Moreover, key aspects and implications of ethics, privacy and law are covered as well. Data science methods and techniques are investigated using case studies and applications throughout the life sciences & health, social sciences, geosciences, and the humanities. More information.