Program and courses Data Science and Society
The program is built on a sound mix of theory and practice. The core principle of the program is learning by doing. You will specialize by following one of four tracks: Business, Governance, Media, or Health.
This one-year program consists of 60 credits (EC):
- 4 core courses (24 EC in total)
- 2 research skills modules (6 EC in total)
- 2 elective courses from one of four tracks: Business, Governance, Media or Health (12 EC in total)
- Master’s thesis (18 EC)
You can start in either end of August or end of January .
You follow these four core courses:
- Data Mining for Business and Governance (6 EC)
- Data Science Regulation & Law (6 EC)
- Machine Learning (6 EC)
- Statistics and Methodology (6 EC)
Research skills modules
You choose two of the following research skills modules:
You choose two electives from the offer that matches your track:
* There may be a maximum number of students for these two electives. As a result, it cannot be guaranteed that you will be able to take these courses.
Master’s thesis / Data Science in Action
You complete the program with a final graduate project, your Master’s thesis, which integrates all relevant aspects of data science in a real life situation. For your thesis research you choose a topic from a cluster of topics linked to your track and previous education. You also have the opportunity to “pitch” a specific thesis topic or bring data and research aims from an external partner.
Examples of previous thesis topics by track:
- Examining the effect of the macroeconomy on financial portfolios with the use of machine and deep learning models
- Predicting Dutch patent validations using machine learning
- Predicting market movements for Bitcoin and Nasdaq-100 using Reddit sentiment
- A deep learning approach to the influence of vocal behaviour on the decision-making process in the entrepreneurial context
- Predicting the online customers' purchase intention comparing machine and deep learning models
- Applying cost-sensitive machine learning models to loan default prediction
- Predicting early retirement intentions of mature workers using machine learning and deep learning algorithms
- Predicting hotel booking cancellations using the XGBoost algorithm
- School dropout prediction in Malawi using household panel data: a machine learning approach
- Predicting Twitter users’ stance on climate change based on local demographic features in the US
- Optimization of cellular automata based models to predict urban densification in the Netherlands
- An application of machine learning models to predict the Covid-19 vaccine coverage of sub-national areas in Flanders using socio-demographic variables
- Where not to park your bike: a comparison of grid thematic mapping and kernel density estimation hotspot analysis in predicting future bicycle thefts.
- Predicting nitrogen concentrations using machine learning techniques
- Detect biometric fraud on portrait images using deep convolution neural network
- Using entity-action-target relationships to classify conspiratorial YouTube Videos
- Predicting deterioration in a Tilburg hospital population with machine learning algorithms
- Detecting heart failure at an early stage to reduce the high mortality rate using various machine learning algorithms
- Improving classification in trauma patients using unsupervised deep learning techniques
- Comparison of convolutional neural network and vision transformer models for breast cancer classification in mammography
- Predicting Montgomery-Åsberg Depression Rating-Scale scores and levels of depressed patients on the basis of motor activity times series data using long short-term memory neural networks
- Detection of Parkinson’s disease from drawings using image preprocessing techniques and machine learning algorithms
- Effects of feedback and role on mother-child brain-to-brain synchrony: an EEG hyperscanning study
- Classification of COVID-19 based on audio signals using reconstructed phase space and deep learning
- Adding simplicity to translation: using compressed texts as a pivot for neural machine translation between sign and spoken language
- EEG functional connectivity during robot-assisted (second) language learning in children
- Automated detection of media bias by language use in English news articles
- Data augmentation for classifying hate speech Tweets using a generative adversarial network
- The looks on their listening faces: predicting investment based on investor’s facial expression in a startup funding pitch
Watch a trial lecture
You will find a detailed description of the courses and required literature in our course catalog.
Please note: programs are subject to change. We advise you to look up the current program in OSIRIS Student at the start of the year.
Do you want to know more about this Master’s program?
Find out more during one of our events
Data Science and Society in short
- The program offers a balanced mix of theory and practice and is aimed at students without (much) prior knowledge of programming and machine learning.
- You are admitted to a specialization track that matches the domain knowledge you have developed in your previous education, ensuring a coherent labor market profile.
- You get acquainted with the data science methodology and the laws and regulations that are relevant for data science and data scientists. You learn data analytics techniques and get trained to gather insights from large and complex data sets . You also learn how to translate those insights into actionable solutions and recommendations within your area of specialization.
Interested in the MSc Data Science and Society?
Check your eligibility and the deadlines for application