Introduction to Data Science for Healthy and Sustainable Consumption
The specialisation aims at combining Consumer Science with the upcoming domain of Data Science. This also includes data acquisition from diverse data sources, handling various types of data, techniques for data cleaning, analyzing, visualising the results, and translating the results into usable knowledge in an ethical sound way. Students will be educated as domain specialists with a good knowledge of how to use data science technology and contribute to health promotion.
Become an expert on Data Science for Healthy and Sustainable Consumption by:
- Explaining and comparing a broad range of relevant data sources and data types in the field of healthy and sustainable consumption.
- Applying data science techniques to a data set in the consumption domain given a problem related to health or sustainability.
- Designing and creating data-driven interventions to improve healthy and sustainable consumer behaviour.
In the first year you combine compulsory courses (like the new course Data Science for Health: Principles) with restricted option courses such as Artificial Intelligence for Food and Health and Data Science for Food and Consumer Behaviour Research. And what about Solving Societal Health Challenges with Data science? In this course you participate in a team to advise an actual company or organisation on a problem in the health domain. You bring together academic insights and practical knowledge and come up with an advice on future actions for that company. The second year is for your thesis and internship.
For timetables, courses and course content of this specialisation and the common part of the master's you can read the Study Handbook.
Over the past few years, there has been an increase in the number of job postings asking for skills in artificial intelligence, data analytics and in data analysis in combination with knowledge on consumption behaviour and healthy lifestyles. We aim for delivering a new kind of professional: professionals who understand both data science techniques, and the domain to which those techniques can be applied.