Exploratory Multivariate Data Analysis (FUN)

Exploratory Multivariate Data Analysis (FUN)
Free Course
Categories
Effort
Certification
Languages
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. An introduction to the R language is sufficient.
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Exploratory Multivariate Data Analysis (FUN)
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 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.

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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.

Suggested Readings:

Exploratory Multivariate Analysis by Example Using R (Chapman & Hall/CRC Computer Science & 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


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



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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Free Course
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. An introduction to the R language is sufficient.

MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.