R - Exploratory Data Analysis using R
Details
| ID | 2858624 |
| Duration | 3.0 days |
| Methods | Presentation with examples and hands-on labs. |
| Prerequisites | Basics in R and Statistics |
| Target group | Data Analysts |
Overview
Exploratory Data Analysis (EDA) is a statistical approach to analyzing data sets to summarize their main characteristics. This training primarily focuses on four main techniques of EDA: Principal Component Analysis (PCA) for quantitative variables, Correspondence Analysis (CA) and Multiple Correspondence Analysis (MCA) for categorical variables and finally (hierarchical and partitioning) clustering methods. As an umbrealla technique, this training also shows Factor Analysis (FA) and Multiple Factor Analysis (MFA). For the hands-on labs and practical examples the participants will use R and esp. FactoMineR - a special R package for the exploratory data analysis.
Dates
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Your benefits at a glance
- Flexible preferred date
- Customized content
- Intensive exchange
- High practical relevance
Comelio Media
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Services
- Lunch / catering
- Help with hotel / travel
- Comelio certificate
- Flexible: free cancellation up to one day before
Content
Principal Component Analysis (PCA)
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 FactoMineRCorrespondence Analysis (CA)
Objectives and the Independence Model - Fitting the Clouds: Row and Column Profiles - Interpreting the Data - Implementation with R and FactoMineRMultiple Correspondence Analysis (MCA)
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 FactoMineRClustering
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 FactoMineRMultiple Factor Analysis (MFA)
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 FactoMineRInstructor
Marco Skulschus (born in Germany in 1978) studied economics in Wuppertal (Germany) and Paris (France) and wrote his master´s thesis about semantic data modeling. He started working as a lecturer and consultant in 2002.Publications
- Grundlagen empirische Sozialforschung (Comelio Medien)
978-3-939701-23-1 - System und Systematik von Fragebögen (Comelio Medien)
978-3-939701-26-2 - Oracle PL/SQL (Comelio Medien)
978-3-939701-40-8 - MS SQL Server - T-SQL Programmierung und Abfragen (Comelio Medien)
978-3-939701-69-9