|Teaching method||Contact hours|
|Course coordinator(s)||prof. dr. EWML de Vet|
|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).
Data Science for Health II and/or data science intensive thesis within the health domain
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):
- Opportunities for retrieving and mining data sources in health sciences
- Expected developments in health data collection and availability
- Main data analyses strategies (hypothesis generating versus hypothesis testing)
- Case studies on clustering, generalised linear and additive modelling, Bayesian modelling, lasso, ridge, elastic net, support vector machines, text mining.
After successful completion of this course students are expected to be able to:
- explain and compare a broad range of relevant data sources in the field of health science
- Identify challenges and opportunities that come with using Big data for health research
- select an appropriate data analysis method based on the characteristics of the data
- discuss relevant issues regarding internal and external validation (such as selection bias, confounding)
- interpret results from data analysis in health science
For data analysis, the R environment is used.
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|
|MNH||Nutrition and Health||MSc||B: Nutritional Physiology and Health Status||6AF|
|MCH||Communication, Health and Life Sciences||MSc|