BIF-51306 Data Analysis and Visualization


Credits 6.00

Teaching methodContact hours
Individual Paper1
Independent study0
Course coordinator(s)dr. ADJ van Dijk
Lecturer(s)dr. ADJ van Dijk
dr. JA Hageman
dr. JJJ van der Hooft
Examiner(s)prof. dr. ir. D de Ridder

Language of instruction:


Assumed knowledge on:

BIF-50806 Practical Computing for Biologists

Continuation courses:

BIF-51806 Biological Discovery Through Computation


Much data is quantitative, and there is a wide range of methods available for the analysis of such data. After a brief introduction to data types and normalisation, a number of visualisation methods will be discussed. Next, methods will be introduced to find groups (clustering), dependencies (regression), significant differences between conditions (hypothesis testing) and to predict classes (classification). In addition, ways of assessing the relevance of findings and of interpreting results will be discussed. Students will learn to apply all these methods in practice in R.

Learning outcomes:

After successful completion of this course students are expected to be able to:
- explain a number of normalisation and visualisation methods for specific types of data and purposes;
- explain qualitatively a number of analysis methods from statistics, clustering and classification;
- given a research question, select appropriate analysis methods;
- implement analysis methods for a specific dataset in R scripts;
- critically evaluate the results of an analysis and their significance;
- explain the influence of parameters and settings chosen for the analysis methods, on the results.


In subsequent blocks, students will learn about normalization, visualization, clustering, regression, hypothesis testing, and classification. Each block contains: 1) lectures and self-study, 2) lab work, and 3) a project on real-world data. The course concludes with an integrative project and a final examination.


Final marks will be based on the marks obtained for the projects in each block (25%), as well as the marks obtained for the final project (25%) and the final examination (50%). For the final project and the final examination, the marks obtained should be at least a 5.0. Re-doing projects from each block is not possible. In case the final mark is not sufficient to pass, the final project and the final examination can be re-sit.


Slides, handouts and book: Tony Fischetti, 'Data analysis with R', second edition, Packt, 2018. ISBN 9781788393720

Compulsory for: WUDSCBSc Minor Data Science2AF
WUBIFBSc Minor Bioinformatics2AF