Load, query and manipulate data with R
Clean up raw data before modeling
Reduce dimensions with principal component analysis (PCA)
Develop functionality of R with user-defined packages

Explore the characteristics of a dataset through visualization
Graph the distribution of data with boxplots, histograms and density plots
Identify outliers

Preliminary processing and preparation of unstructured data for further analysis
Describe a set of documents with a term-document matrix

Examine MapReduce and Hadoop architectures
Integrate R and Hadoop with RHadoop

Model the relationship between an output variable and several input variables
Correctly interpret the coefficients of continuous and qualitative data

Process large datasets with RHadoop
Create regression modules for RHadoop

Use decision trees to predict target values
Apply probability rules to predict outcomes with the Naive Bayes model
Combine predictor variables of trees and random forests in RHadoop

Visualize model performance with an ROC curve
Evaluate classification models with confusion matrices

Segment the customer market with the K-Means algorithm
Find similarities with distance measures
Create tree-shaped clusters and hierarchical clusterings
Cluster tweets and text files to better understand them

Identify important connections with social media analytics
Understand the use of social media analytics results for marketing purposes

Identify real customer preferences from a set of transactional data to improve user experience
Calculate support and trust indices and lift to differentiate good rules from bad ones