|Teaching method||Contact hours|
|Course coordinator(s)||prof. dr. EWML de Vet|
|prof. dr. ir. EJM Feskens|
|Lecturer(s)||prof. dr. ir. EJM Feskens|
|prof. dr. EWML de Vet|
|dr. G Camps|
|prof. dr. HC Boshuizen|
|Examiner(s)||prof. dr. EWML de Vet|
|prof. dr. ir. EJM Feskens|
Language of instruction:
Assumed knowledge on:
This course assumes a good working knowledge of statistics such as from Advanced Statistics (for Nutritionists), Research Methods and Data Analysis in Communication and Health (YRM30806) and/or Statistics for Data Science, as well as knowledge of the health domain.and of data science concepts (e.g. through the Concepts in Data Science course). This course builds upon Data Science for Health I and that is assumed knowledge.
Data science intensive thesis
In many areas of biological, environmental and social science new tools and strategies are developed to measure multiple features at subjects and objects of interest. Typical for these new types of data is that they occur in large volumes, are high dimensional and occur at various levels in a hierarchy of data types.
In nutrition and health sciences the effects of diet and lifestyle variables can be investigated with respect to physical and mental indicators of performance and well-being, and disease and mortality outcomes. In both the exposures (e.g. dietary intake: what, where, when?; physical activity what, where, when) and outcomes (e.g. dimensions of health) these high dimensional data occur. Moreover, data are continuously and automatically gathered that help to predict the exposures (e.g., through GIS, sensing, smartphones, social media). Data science is a new science that combines elements of statistics, mathematics, computer science and substantive knowledge. Data science can be used to generate and investigate relevant research questions on causes and consequences of diet and lifestyle variables. In this course students learn about the opportunities and challenges for Big data in health research.
In this course the focus will be on data in the field of nutrition and health and health and society.
Course topics (as this course is currently under development, topics may be included or replaced according to recent developments in the field):
- Scripting and data analysis for a selected number of cases within this domain
- Critical reflection on the interpretation of analysis outcomes
After successful completion of this course students are expected to be able to:
- apply data analysis methods for data science in health (using R)
- evaluate the reliability of the outcomes of an analysis
- interpret, visualize and communicate results from data analysis to a multidisciplinary data science team
Lectures, tutorials, data analysis practicals and (individual and/or group) assignments
As this is a brand new course, the assessment strategy is not fixed yet. Details will be elaborated in the course guide.
To be announced
|Restricted Optional for:||MNH||Nutrition and Health||MSc||A: Nutritional and Public Health Epidemiology||6AF|