Multivariate data analysis Level 1

main objectives

Be able to ‘identify’ data, organize it and then use appropriate data analysis methods (descriptive methods of multivariate analysis),Be able to analyze large tables,Being able to understand and apply the mathematical underpinnings of the methods used,Know how to use R software analysis scripts (https://www.r-project.org/),Be able to describe and interpret the results obtained using R software,To be able to present your results in public in the form of a slideshow ,

general content

Generalities (different types of variables, tables, coding variables),Multidimensional and descriptive methods in 1 table: Principal Component Analysis (PCA), Correspondence Analysis (CA) & Multiple Correspondence Analysis (MCA).,Multidimensional and descriptive methods in multiple tables: Multiple Factor Analysis (MFA).,Distances (indexes of similarities, dissimilarities, usual distances),Clustering methods (HCA, K means ...),

pedagogy

6 CM sessions of 1.5 hours ,2 TD sessions of 1.5 hours to upgrade the use of R software,& 6 TD sessions of 1.5 hours ,,A laptop with a web browser and Wi-Fi access to ESA’s network is essential in TD,

evaluation

Written exam : ,Interpreting the results of a dataset analysis ,Evaluation of knowledge on one of the multidimensional methods,