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MOOC Exploratory Multivariate Data Analysis

Inscriptions ouvertes jusqu'au 19 avril 2019

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.

MOOC sur FUN-MOOC - du 4.03.19 au 13.05.2019
Auteur(s): François Husson, Jérôme Pagès, Magalie Houée-Bigot

Présentation du MOOC Exploratory Multivariate Data Analysis

Informations pratiques sur le MOOC Analyse des données multidimensionnelles

  • Type: MOOC, cours en ligne, quiz autocorrectifs, exercices sur table et mini-projets, attestation de suivi
  • Temps d'apprentissage: 5 semaines
  • Niveau: à partir du niveau master
  • Durée: 05:00h/semaine
  • Langues: anglais
  • Contenu: vidéos
  • Public cible: étudiants et professionnels ayant un niveau master et/ou des connaissances dans une discipline scientifique
  • Age attendu: 21 et +
  • Droits: Licence Creative Commons BY-NC-ND

Description du MOOC Analyse des données multidimensionnelles

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 (P.C.A.) when variables are quantitative
  • correspondence analysis (C.A.)
  • multiple correspondence analysis (M.C.A.) when variables are categorical and clustering

An extension to Multiple Factor Analysis (M.F.A.) 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.

Plan du MOOC

  • 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

Évaluation

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.

Informations pédagogiques

Public

  • Public visé : 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.

  • Pré-requis : 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.

Édition et diffusion du MOOC Exploratory Multivariate Data Analysis

Édition

Diffusion

Conditions d'utilisation

  • Du cours : Licence Creative Commons BY-NC-ND. The user must give appropriate credit, may not use the material for commercial purposes and may not distribute a modified material. 
  • Du contenu produit par les internautes : Restrictive license: your production remains your intellectual property and can therefore not be reused. 

Documents annexes - MOOC Exploratory Multivariate Data Analysis

Lecture recommandée

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.
Publication : 29.01.2019