Master's Data Science for Food and Health
Data are everywhere: from sensors and trackers to apps measuring the behaviour of people. The use of data science offers new opportunities in the domains of health, nutrition, lifestyle and consumer behaviour. We can use data science to better measure and understand what and how people eat. What food choices do they make? And what is the relation between lifestyle and human health? You will be able to integrate data science knowledge and skills with a sound understanding of nutrition, consumer behaviour and lifestyles, and their effects on human health. After graduation, a data scientist in Food and Health will have a solid knowledge base, as well as excellent connecting skills.
Why this programme?
- Interdisciplinary programme which integrates data science knowledge and skills with a sound understanding of nutrition, consumer behaviour and lifestyles, and their effects on human health.
- Learn to translate raw data from diverse sources into intelligible and actionable knowledge, using smart data processing and analytical methods.
- Explicit attention for interdisciplinary skills needed to perform a bridging role between various stakeholders.
- Personal leadership: students need to design and plan their individual learning pathway during their studies and will learn how to give direction to their personal and professional development.
Study programme of MSc Data Science for Food and Health
The Master's programme in Data Science for Food and Health will train ‘bridge builders’ who specialise in applying statistical and analytical instruments to Wageningen domains such as Human Health and Nutrition, Consumption and Healthy Lifestyles, and Marketing and Consumer Behaviour.
On the Programme of Data Science for Food and Health page you can find the general outline of the programme and more detailed information about courses, internship and thesis.
Trajectories
The Data Science for Food and Health programme contains four learning trajectories. Here you can read more about these trajectories.
- Data science learning trajectory: focuses on the application of the complete data science lifecycle
- Food and Health learning trajectory: focuses on consumer behaviour, nutrition or lifestyle
- Integration and translation learning trajectory: connects the disciplines of data science and the application domain
- Research learning trajectory: prepares for independently conducting research
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Application and admission
Interested in taking part in the programme of Data Science for Food and Health? Find out more about the specific Admission requirements and the application procedures.
The next generation data scientist: using data with a purpose in life
Future career
What are your possibilities after graduating? Read more about Career perspectives and opportunities after finishing the programme.