• End of Registration
  • may 01 2021
  • Classes Start
  • mar 03 2021
  • Classes End
  • may 11 2021
  • Estimated Effort
  • 05:00 h/week
  • Language
  • English

About this course

This 6th edition of the MOOC starts on March 3, 2021.

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. An extension to Multiple Factor Analysis (MFA) will give you the opportunity to analyse more complex dataset that are structured by groups.

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.

To whom is this course addressed?

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. It is therefore addressed to practitioners who are confronted with the analysis of data in marketing, surveys, ecology, biology, geography, etc.


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.

Pedagocial team

François Husson

Professor of statistics at the Applied Mathematics Department in Agrocampus Ouest (Rennes), François Husson has published several books in French and in English and has developed the R package FactoMineR.

Magalie Houée-Bigot

Teaching assistant in statistics at the Applied Mathematics Department in Agrocampus Ouest (Rennes), Magalie Houée-Bigot has developed several packages for the R software and teaches exploratory multivariate data analysis.

Course Schedule

Week 1. Principal Component Analysis
  • Data - Practicalities
  • Studying individuals and variables
  • Aids for interpretation
  • PCA in practice using FactoMineR
Week 2. Correspondence Analysis
  • Data - introduction and independence model
  • Visualizing the row and column clouds
  • Inertia and percentage of inertia
  • Simultaneous representation
  • Interpretation aids
  • Correspondance Analysis in practice using FactoMineR
Week 3. Multiple Correspondence Analysis
  • Data - issues
  • Visualizing the point cloud of individuals
  • Visualizing the point cloud of categories - simultaneous representation
  • Interpretation aids
  • Multiple Correspondance Analysis in practice using FactoMineR
Week 4. Clustering
  • Hierarchical clustering
  • An example, and choosing the number of classes
  • Partitioning methods and other details
  • Characterizing the classes
  • Clustering in practice using FactoMineR
Week 5 : Multiple Factor Analysis
  • Data - issues
  • Balancing groups and choosing a weighting for the variables
  • Studying and visualizing the groups of variables
  • Visualizing the partial points
  • Visualizing the separate analyses
  • Taking into account groups of categorical variables
  • Taking into account contingency tables
  • Interpretation aids
  • Multiple Factor Analysis in practice using FactoMineR


Participants will focus on one theme per week and will have the opportunity to evaluate their learning progress via a weekly quiz. Each course sequence, will be completed by a series of small quizzes and exercises. You will do your exercises directly in your web browser, and the correctness of your answer will be automatically assessed by the system.
At the end of the course, you will have to complete a final evaluation and participants who have more than 50% of correct answer in quizzes and exercises will receive a certificate of attendance


This course is available in the book:
Husson, F., Pagès, J. et S. Lê (2017). Exploratory Multivariate Analysis by Example Using R. CRC/PRESS, 2nd edition.
The second edition will be available in 2017.

Course content

BY-NC-ND Creative Commons License: the user must give appropriate credit, may not use the material for commercial purposes and may not distribute a modified material.

Content produced by the participants

Restrictive license: your production remains your intellectual property and can therefore not be reused.