Data Analysis and Visualization

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:

• describe qualitatively a number of analysis methods from statistics, clustering, and classification;
• apply the correct normalization and visualization methods given specific data types and research questions;
• implement quantitative analysis methods for a specific dataset in R scripts;
• interpret analysis results and their statistical significance and relevance through observation;
• assess the outcome of data analysis with different parameters and settings using appropriate measures and visualization methods;
• select the appropriate analysis methods to answer a given domain-specific research question.

Assumed Knowledge:
BIF50806 Practical Computing for Biologists or INF22306 Programming in Python