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There's something about the hive being in the middle of a graph that's particularly revealing to a book about analytics: It reads like a recipe book for tools that are designed to be stacked on top of one another. The discussion of the motivation and interdependencies of the various tools that comprise a working dataset in the library is intended to serve as a hands-on tutorial to data wrangling. This is probably why so many small and mid-size businesses choose to keep things simple by using something like Google Sheets as a stand-in for a structured, relational data store. The real problem is that the statistics and analytics community does not currently have a tool that produces a highly interactive environment in which people can run software without the need for software installation. As noted in Chapter 2, SciStarter allows researchers to showcase their work in public spaces, but the software is designed as a one-off for the current day, and SciStarter's servers are under the domain of the National Science Foundation. The networking capabilities on top of a Google Sheet don't allow for sharing code, and therefore the code is effectively permanent. ## 4 ## Machine Learning and Data Analytics at CERN




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