Exploratory Multivariate Data Analysis

About this course

This 5th edition of the MOOC starts on March 2, 2020.

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.

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