Why this post Basically, my analytical toolbox consists of a few basic things:
Programming Languages (R and python) Storage (MySQL and MongoDB) Visualization and Reporting (Tableau Desktop) If I even remotely resembled a Data Scientist, other tools listed above would include the all things command line, Hadoop, d3, and some familiarity with AWS. With that said, you can do some really cool research with the tools listed above.
I long have wanted to post about the things I am doing with R and Google Analytics for my division, so I was very excited to see that tonight’s #emchat is on web analytics. This post will be more on the technical end, but I wanted to highlight what’s possible beyond the basic point-and-click interface in Google Analytics.
Why do this post? Google, as well as other vendors, provide APIs to our web data.