Data Reduction Techniques Using Excel and R: Business Analytics Deep Dive

Data Reduction Techniques Using Excel and R: Business Analytics Deep Dive

With businesses having to grapple with increasing amounts of data, the need for data reduction has intensified in recent years. To make sense of an overabundance of information, you can use cluster analysis—which allows you to develop inferences about a handful of groups instead of an entire population of individuals—as well as principal components analysis, which exposes latent variables.

In this course, We explain how to carry out cluster analysis and principal components analysis using Microsoft Excel, which tends to show more clearly what's going on in the analysis. Then we explain how to carry out the same analysis using R, the open-source statistical computing software, which is faster and richer in analysis options than Excel. Plus, we walk through how to merge the results of cluster analysis and factor analysis to help you break down a few underlying factors according to individuals' membership in just a few clusters.

Topics include:

• Reviewing the problems created by an overabundance of data
• Understanding the rationale for clustering and principal components analysis
• Using Excel to extract principal components
• Using R to extract principal components
• Using R for cluster analysis
• Using Excel for cluster analysis
• Setting up confusion tables in Excel
• Using cluster analysis and factor analysis in concert
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• Reach out to us for full details outreach@opencastlabs-africa.com