The teaching programme consists of two terms. In the first term, from September to the end of December, you will take four courses of 6 ec each:
- Behavioural Data Science Toolbox
- Big Data Analytics
- 1 Elective course: Network Analysis or Deep Learning in Python
The second term, which lasts from January to the end of June, you will start with the Internship Data-Driven Consultancy. This consists of two parts. The first part is a consultancy project for a real client, this part is offered in January. For the second part, students need to participate a few times as a consultant in the Methodology Shop. The office hours are spread over the whole year, and students will be scheduled beforehand.
Behavioural Data Science Toolbox???studyprogramme .period??? 16
Big Data Analytics???studyprogramme .period??? 16
Psychometrics???studyprogramme .period??? 26
Internship Data-Driven Consultancy???studyprogramme .period??? 33
Master’s Internship Behavioural Data Science3—618
Master’s Thesis Behavioural Data Science3—618
Restricted-choice electives: Behavioural Data Science???studyprogramme .period??? 26
Internship and thesis
The purpose of the thesis (18 EC) is to answer a scientific question relating to behavioural data science. It is possible to combine the external internship and the thesis into one project (36 EC).
During the Internship Data-Driven Consultancy (3 EC), you will carry out a real-life data science project under supervision, and you will serve as an advisor to fellow students regarding questions about research methodology and statistics.
During the main external internship (15 EC), you will work for a company or (public) institution and gain experience in applying your acquired knowledge and skills to real-life challenges. You are encouraged to find and organise the external internship independently, but we also host a yearly internship event in collaboration with Amsterdam Data Science.
The programme reserves 6 EC for electives. In Semester 1, block 2, you can choose 1 out of the following two electives: Network Analysis or Deep Learning in Python . More info can be found in the course catalogue.