An Introduction to the Glut

As a relatively new convert to the field of data science, I find myself repeatedly and simultaneously excited, impressed, and humbled by the sheer volumes of data available to those willing to dig for it. The almost non-existent latency with which new information is gathered, stored, and disseminated globally is surely not a reality previous generations could have predicted.

Finding meaning in the veritable ever-accumulating glut of data that fills our lives, however, is no small feat. For that reason, I've chosen to chronicle my mining and munging, my scrubbing and scraping, in an attempt to share outcomes that I feel are either meaningful, interesting, or "cool." I hope and fully intend for my exposition of the process to be as intriguing as the outcomes themselves. After all, the more mundane the data set, the more extraordinary and creative of an approach required to transform that data into knowledge.

I plan to use a combination of relevant applications such as Alteryx and Tableau (both growing in popularity and power with each release) for data manipulation and visualization respectively. I fully expect Python and R to make their way into posts as well. I'm of the opinion that the future belongs to those who can both program software and perform statistical analysis. The days are numbered for statisticians incapable of implementing a MapReduce algorithm as well as for programmers able to write the algorithm but fall short when time comes to interpret the true value of the results. For that reason, I find that a combination of the tools above are almost limitless in their ability to acquire, crunch, and convey information: a proper data scientist's toolbox indeed.

Despite being at risk of becoming the often cited "jack of all trades, master of none," I find myself eager to test all the facets of being a modern data scientist before settling down into a deep trough of specific expertise. Hopefully you'll enjoy the escapades documented here as much as I do.