R is a high-level language and an environment fordata analysis and data visualization. While R can be used as acalculator and all important basic statistical methods are included aswell, the main benefit is its open-source philosophy which makes Rhighly extensible and renders possible the availability of new, cuttingedge applications in many different fields. The popularity of Rincreased constantly during the last years and by now, it is arguablythe most popular software for data analysis in the statisticalcommunity. The course starts with an introduction to R, covers someelementary statistical techniques and then continues with more advancedmethods. In particular, the course will focus on:

• Linear Models (Standard Linear Regression and Analysis of Variance)
• Binary and Multinomial Logistic Regression
• Decision Trees
• Naive Bayes Classi ers
• Association Rules
• Cluster Analysis
• Correspondence Analysis 
•  Principal Components Analysis and Factor Analysis
• Social Network Analysis

On completion of the course students are able to:

• manipulate and visualize data in R
• understand the theory and functionality of the methods employed in the course
• apply the adequate statistical methods to a given problem and perform the statistical calculations using R
• interpret the results of such analyses
• communicate and discuss the results of the statistical analysis of data

• Participation in class
• written report on an analysis
• oral presentation of analysis results