Welcome to R-bar, the blog dedicated to exploring topics at the interface of Datascience, Statistics, Automation and R.

This blog was started on January 1, 2017 (yes, there is  a New Year’s Resolution involved) and over the coming months, the goal is to cover topics such as …

  • R language nuances and trends
  • Statistics  (with a practical bias towards quality control)
  • Datamining & Machine Learning (Clustering, Neural Networks … )
  • Automation  involving Arduino, RasberyPi, and R/python.




About a year after leaving graduate school, I was sitting in a face-to-face meeting with a customer who was reviewing their  progress on integrating my employer’s technology in their organization. During the meeting, their analytically savvy technology lead spoke about how they had used a numerical analysis method, “PCA”, to help determine the root cause of a problem they’d been having. Being the scientific analytical lead in my organization, and coming from a well known graduate institution, I was caught off guard – why hadn’t I heard of this problem solving method? After the meeting, I began looking into PCA. I learned the acronym stood for principle component analysis but quickly my searches led me into uncharted territory, with results involving matrices, singular value decomposition, and dimensional reduction. By this point, I felt daunted and knew I had a great deal to learn before I could even get close to understanding what was going on with PCA. This realization marked the beginning of my decent into statistics and data science.


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