MOOC Exploratory Multivariate Data Analysis

Descriptif de la formation

Exploratory multivariate data analysis is studied and teached in a French-way since a long time in France. This course focuses on four essential and basic methods, those with the largest potential in terms of applications: principal component analysis (PCA) when variables are quantitative, correspondence analysis (CA) and multiple correspondence analysis (MCA) when variables are categorical and clustering.

This course is application-oriented; formalism and mathematics writing have been reduced as much as possible while examples and intuition have been emphasized and the numerous exercises done with FactoMineR (a package of the free R software) will make the participant efficient and reliable face to data analysis.

We hope that with this course, the participant will be fully equipped (theory, examples, software) to confront multivariate real-life data.



An undergraduate level is quite sufficient to capture all the concepts introduced.

Basic knowledges in statistics are necessary, such as: correlation coefficient, chi-squared test, one-way ANOVA.

On the sofware side, an introduction to the R language is sufficient, at least at first.

Contenu de la formation

Week 1. Principal Component Analysis
Week 2. Correspondence Analysis
Week 3. Multiple Correspondence Analysis
Week 4. Clustering

Compétences visées

This course will be held in English. It has been designed for scientists whose aim is not to become statisticians but who feel the need to analyze the data themselves.