Data analysis ENS T109 – 2018/2019


October 8th to 12th, 2018


Course objectives and description 

Biological data are often complex and challenging to analyse due to non-normal distributions, nonlinear relationships, spatial/temporal structures and high dimensionality. This course will introduce the students to key concepts and statistical tools for the experimental design and analysis of biological data. After a brief refresher on basic elements of statistics, the students will be made familiar with hypothesis testing, univariate statistical tests (e.g. ANOVA), linear models, descriptive multivariate analyses such as Principal Component Analysis (PCA) and clustering. The course will alternate theoretical aspects and computer exercises on small datasets with the R Studio software. The students will be assigned a small project involving the different concepts and tools covered by the course. Students are expected to bring their own laptops the afternoons of Wednesday 10th and Friday 12th October.


Course materials are available hereafter


Correction of MCQs