Complete course on Exploratory Multivariate Data Analysis (MOOC)

This course corresponds to a MOOC (Massive Open Online Course) that is free and played in February or March (the first session was in 2017). You can subscribe From December to March from the platform FUN.

The links in blue correspond to the course videos or the course slides on the method, the links in brown correspond to tutorial's on FactoMineR or on missing values.

Note that sometimes we refer to quiz and exercises but they are only available on the MOOC.


1. Principal Component Analysis (PCA)

  1. Data - practicalities
  2. Studying individuals and variables
  3. Interpretation aids
  4. PCA with FactoMineR 
  5. Factoshiny: interactive graphs in exploratory multivariate data analysis 
  6. Handling missing values in PCA

2. Correspondence Analysis (CA)

  1. Introduction
  2. Visualizing the row and column clouds
  3. Inertia and percentage of inertia
  4. Simultaneous representation
  5. Interpretation aids
  6. CA with FactoMineR 
  7. Text mining with correspondence analysis 

3. Multiple Correspondence Analysis (MCA)

  1. Data - issues
  2. Visualizing the point cloud of individuals
  3. Visualizing the cloud of categories
  4. Interpretation aids
  5. MCA with FactoMineR
  6. Handling missing values in MCA

4. Clustering

  1. Introduction
  2. Example and how to choose the number of clusters
  3. The partitioning method K-means
  4. Characterizing clusters
  5. Clustering with FactoMineR

5. Multiple Factor Analysis

  1. Introduction
  2. Weighting and global PCA
  3. Study of the groups of variables
  4. Complements: qualitative groups, frenquency tables
  5. MFA with FactoMineR

To conclude