MOOC 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.
Book
| The book Exploratory Multivariate Analysis by Example Using R provides a description of the methods seen during this MOOC. For more information or to order the book. |