The goal of the programme is to equip you with in-depth knowledge of relevant big data and statistical learning techniques, psychological measurement, along with a broad set of practical skills so that you can successfully start a career in data science. The programme contains three mandatory courses (18 EC), one or two elective courses (6 EC), a client consultancy case (group project; 3EC), one external individual internship (15 EC) and a thesis (18 EC).
The three mandatory courses are Behavioural Data Science Toolbox, Big Data Analytics, and Psychometrics.
The course Behavioural Data Science Toolbox 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 (tidyverse and SQL), the principles of data visualisation, and training involving some state-of-the-art visualisation tools (e.g. Tableau, ggplot, Shiny).
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 of applications using multiple software tools (R, Excel, SQL).
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 reserves 6 EC for electives. The two possible electives include Deep Learning in Python (under construction) or Network Analysis. More info can be found in the course catalogue.
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.