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training duration icon R - Exploratory Data Analysis using R

Duration: 3 Days
Delivery Type: Classroom
Target Audience: Data Analysts
Course Number: 1000032
Method: Presentation with examples and hands-on labs.
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A. Principal Component Analysis (PCA)

[Duration: 0.75 Days] Objectives of PCA and Introduction to PCA - Studying Individuals: The Cloud of Individuals, Fitting the Cloud of Individuals - Variables: The Cloud of Variables, Fitting the Cloud of Variables - Relationships - Interpreting the Data - Testing the Significance of the Components - Implementation with R and FactoMineR

B. Correspondence Analysis (CA)

[Duration: 0.25 Days] Objectives and the Independence Model - Fitting the Clouds: Row and Column Profiles - Interpreting the Data - Implementation with R and FactoMineR

C. Multiple Correspondence Analysis (MCA)

[Duration: 0.25 Days] Objectives: Studying Individuals, Variables, and Categories - Defining Distances between Individuals and Distances between Categories - CA on the Indicator Matrix: Relationship between MCA and CA, The Cloud of Individuals, Variables, and Categories - Implementation with R and FactoMineR

D. Clustering

[Duration: 0.75 Days] Concepts of Similarity and Distance: Similarity between Individuals and Groups - Ward's Method - Partitioning and Hierarchical Clustering - Direct Search for Partitions: K-means Algorithm - Clustering and Principal Component Methods - Implementation with R and FactoMineR

E. Multiple Factor Analysis (MFA)

[Duration: 0.75 Days] Factorial Analysis of Mixed Data - Weighting Groups of Variables - Comparing Groups of Variables and Indscal Model - Qualitative and Mixed Data - Multiple Factor Analysis and Procrustes Analysis - Hierarchical Multiple Factor Analysis - Implementation with R and FactoMineR

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For trainings with hands-on labs you are kindly requested to bring your own laptop with the required software. Alternatively, you can use our virtual machines (VMWare) for the MS Windows OS.
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