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Department of Statistics
and Computer Science










4 MOOCs

3 MOOCS in French and 1 MOOC in English

MOOC d'analyse de données (in French)

MOOC (Massive Open Online Course) disponible sur la plateforme FUN et suivi par plus de 38 000 personnes (1 session par an depuis 2015). Les sessions sont jouées en férvier-mars tous les ans, vous pourrez vous inscrire sur FUN.
Ce cours complet est un module qui nécessite environ 32h d'apprentissage.
MOOC d'analyse de données

MOOC de plan d'expériences (in French)

Ce cours complet correspond à un module de master 1 de 28h.
MOOC de Planification expérimentale

MOOC de sensométrie (in French)

Ce cours complet correspond à un module de master 1 de 28h.
MOOC de sensométrie

Course on Exploratory Multivariate Data Analysis (in English)

This course is a complete course on Exploratory Multivariate Data Analysis. You will find courses on the following method:

  1. PCA - Principal Component Analysis
  2. CA - Correspondence Analysis
  3. MCA - Muliple Correspondence Analysis
  4. Clustering
  5. MFA - Multiple Factor Analysis

PCA - Principal Component Analysis

PCA is a well known method for exploring and visualizing data. The function Factoshiny of the package Factoshiny allows you to perform PCA in a really easy way. You can include extras information such as categorical variables, manage missing data, draw and improve the graphs interactively, have several numeric indicators as outputs, perform clustering on the PCA results, and even have an automatic interpretation of the results. Finally, the function returns the lines of code to parameterize the analysis and redo the graphs, which makes the analysis reproducible.

Implementation with R software

See this video and the audio transcription of this video:

PCAFacto

Course videos

Theorectical and practical informations on PCA are available in these 3 course videos:
  1. Data - practicalities
  2. Studying individuals and variables
  3. Interpretation aids
Here are the slides and the audio transcription of the course.

Materials

Here is the material used in the videos:

Missing data

And here is a video that gives more information on the management of missing data.

CA - Correspondence Analysis

Correspondence Analysis is a method for exploring and visualizing contingency tables. It is particularly useful in text mining.

Implementation with R software

See this video and the audio transcription of this video:

PCAFacto

Course videos

Theorectical and practical informations on Correspondence Analysis are available in these 6 course videos:
  1. Introduction
  2. Visualizing the row and column clouds
  3. Inertia and percentage of inertia
  4. Simultaneous representation
  5. Interpretation aids
  6. Text mining with correspondence analysis
Here are the slides and the audio transcription of the course.

Materials

Here is the material used in the videos:

MCA - Multiple Correspondence Analysis

Multiple Correspondence Analysis is a method for exploring and visualizing datasets with categorical variables. Usually, datasets obtained from a survey or a questionnaire.

Implementation with R software

See this video and the audio transcription of this video:

PCAFacto

Course videos

Theorectical and practical informations on Multiple Correspondence Analysis are available in these 4 course videos:
  1. Data - issues
  2. Visualizing the point cloud of individuals
  3. Visualizing the cloud of categories
  4. Interpretation aids
Here are the slides and the audio transcription of the course.

Materials

Here is the material used in the videos: And here is a video that gives more information on the management of missing data in MCA.

Clustering

Clustering is a method that proposes to construct a hierarcchical tree, to draw a partition of individuals, and to describe and characterize the clusters done.

This video answers the following questions: How to perform clustering and draw a hierarchical tree? How you can make a partition? How can we describe and characterize the clusters? How to make a partition with lots of individuals? How to combine Kmeans and clustering? And how considering categorical variables or contingency tables?

Implementation with R software

See this video and the audio transcription of this video:

CLASSIFFacto

Course videos

Theorectical and practical informations on clustering are available in these 4 course videos:
  1. Introduction
  2. Example and how to choose the number of clusters
  3. The partitioning method K-means
  4. Characterizing clusters
Here are the slides and the audio transcription of the course.

Materials

Here is the material used in the videos:

MFA - Multiple Factor Analysis

Multiple facrtor analysis deals with dataset where variables are organized in groups. Typically, from data coming from different sources of variables. The method highlights a common structure of all the groups, and the specificity of each group. It allows to compare the results of several PCAs or MCAs in a unique frame of reference. The groups of variables can be continuous, categorical or can be a contingency table.

Implementation with R software

See this video and the audio transcription of this video:

MFAFacto

Course videos

Theorectical and practical informations on Multiple Factor Analysis are available in these 4 course videos:
  1. Introduction
  2. Weighting and global PCA
  3. Study of the groups of variables
  4. Complements: qualitative groups, frenquency tables
Here are the slides and the audio transcription of the course.

Materials

Here is the material used in the videos:

To conclude