The goal of the programme is to equip students with in depth knowledge of relevant big data and statistical learning techniques, psychological measurement, and a broad set of practical skills so that they can successfully start a career in data science. The programme contains four mandatory courses (18 EC), one to two elective courses (6 EC), two internships (3 + 15 EC), and a thesis (18 EC).
In the Course Catalogue you can find a description of the contents of the programme and the description of the individual courses.
The four mandatory courses are Introduction to Behavioural Data Science, Big Data Analytics, Data Visualisation, and Psychometrics.
The course Introduction to Behavioural Data Science gives an overview of different kinds of data science projects, and focuses on several skills that are necessary for effectively solving a client’s problem. It includes extensive interview training, some data wrangling programming skills, and an introduction to visualising statistical results. The course Big Data Analytics focuses on the most popular statistical and machine learning techniques necessary for extracting insights from large amounts of data. These techniques are discussed within the context of statistical methods taught in bachelor courses and are applied to a wide range applications using multiple software tools (R, Excel, SQL). The course Data Visualisation encompasses the principles of data visualisation, and a training of some state-of-the-art visualisation tools (e.g. Tableau, ggplot, Shiny). The course Psychometrics discusses methods to measure human behaviour, and connects well-known psychometric techniques to the machine learning vocabulary. These techniques are applied to data obtained from different kinds of psychological and educational tests.
The programme contains 6 EC for one or two elective courses. These courses may be about statistical techniques, (e.g. Network Analysis, Structural Equation Modeling, or Multilevel Modeling), programming skills, or other topics of interest that are related to behavioural data science.
During the Internship Data-Driven consultancy (3 EC) the students will carry out a real-life data science project under supervision, and they will serve as advisor for follow students regarding questions about research methodology and statistics. During the main external internship (15 EC), students will work for a company or (governmental) institution and gain experience in applying their acquired knowledge and skills to real life problems. Students are encouraged to find and organize the external internship independently.
The purpose of the thesis (18 EC) is to answer a scientific question related to behavioural data science. It is possible to combine the internship and the thesis into one project (36 EC).