Exploratory Multivariate Data Analysis

What you will learn

At the end of this course, you will be able to:

how to summarise and synthesise datasets using simple graphs
how to use visualization methods adapted to multidimensional exploratory analysis
how to interpret the results of a factor analysis and a classification;
how to ecognise the method adapted to the exploration of a dataset according to the nature and structure of the variables;
how to analyse the responses to a survey;
how to perform a textmining
how to implement factorial and classification methods on the free software R

In summary, you will be able to implement and interpret multidimensional exploratory analyses.

Description

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.

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

Format

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.

Prerequisites

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.

Assessment and certification

To follow this course, you have the choice between two formulas. The DISCOVERY path gives you access to videos, quizzes and exchanges in the forum. Additionnaly, the QUALIFYING path gives you access to a qualifying exam.

- Discovery path

If you opt for this path, you will have access to the videos, the quizzes, the self-corrected exercises and the exchanges in the forum. For this path, no certificate will be delivered. The registration is free.

- Qualifying path

In addition to the activities offered in the DISCOVERY path, the QUALIFYING formula will allow you to obtain a certificate in the form of a "certificate". To do this, you will have to take an exam, monitored remotely, lasting 1 hour and 30, consisting of 20 multiple choice questions (MCQ) and obtaining 10 correct answers.

The registration fee for the qualifying course is 60€.

Course plan

- Data - Practicalities
  Studying individuals and variables
  Aids for interpretation
  PCA in practice using FactoMineR
- 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
- 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
- Hierarchical clustering
  An example, and choosing the number of classes
  Partitioning methods and other details
  Characterizing the classes
  Clustering in practice using FactoMineR
- 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

Course team

François Husson

Magalie Houée-Bigot

Organizations

l'Institut Agro Rennes-Angers

Agreenium

License

License for the course content

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