As with any project or experiment, infrastructure has to be in place to support the intended work. For the case of a data science project, the obvious first step is the computing environment. Simply stated, you can’t do advanced analytics on large data sets without CPU, RAM and Disk. With these items as your foundation, much can be designed, engineered and built. Before we can walk through a data science project we need to first have hardware and software in place. For the purposes of the tutorials here on DataTechBlog, a P.C. or laptop with adequate CPU, RAM and disk will suffice. Further, it is my plan to use only open or free software and code for all tutorials. You need only a reasonably spec’d computer to accomplish all that we will do here. This tutorial will walk you through the installation of VMWare Player and CentOS 6.x (Optimized for Pivotal Greenplum). This lays the foundation for the next steps which will include the installation of Pivotal Greenplum, MADlib libraries, R, and R-Studio. When this environment is complete, you will be able to perform many types of “in database” analysis using SQL with MADlib, analysis using R with Greenplum, and analysis with R against flat files or manually entered data.