|Teaching method||Contact hours|
|Course coordinator(s)||dr. ADJ van Dijk|
|Lecturer(s)||prof. dr. ir. D de Ridder|
|dr. JA Hageman|
|dr. ADJ van Dijk|
|Examiner(s)||prof. dr. ir. D de Ridder|
Language of instruction:
Assumed knowledge on:
BIF-50806 Practical Computing for Biologists
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.
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, classification and interpretation. Each block contains: 1) lectures and self-study, 2) lab work, 3) a project on real-world data and 4) self-study and a test. The course concludes with an integrative project and a final written examination.
Final marks will be based on the marks obtained for the projects (25%) and tests (25%) in each block, as well as the marks obtained for the final project (25%) and the written examination (25%). For the final project and the written examination, the marks obtained should be at least a 5.0. Re-sitting separate tests or re-doing projects from each block is not possible. In case the final mark is not sufficient to pass, the final project and the written examination can be re-sit.
Slides, handouts and book: Tony Fischetti, 'Data analysis with R', Packt, 2015. ISBN 9781785288142; as e-book via https://www.packtpub.com/
|Compulsory for:||WUDSC||BSc Minor Data Science||2AF|
|WUBIF||BSc Minor Bioinformatics||2AF|