• Fin d'inscription
  • 04 avr 2017
  • Début du Cours
  • 27 fév 2017
  • Fin du cours
  • 06 mai 2017
  • Effort estimé
  • 05:00 h/semaine
  • Langue
  • Anglais

About this course

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.

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.

Prerequisite

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.

Jérôme Pagès

Professor of statistics at the Applied Mathematics Department in Agrocampus Ouest (Rennes) until 2014. Jérôme Pagès studied and published papers and books in exploratory multivariate data analysis.

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

Evaluation

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

Reading

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.