self stands for Simple Exploratory Learning Framework. Goal of this project is to provide a simple package with which a novice as well as an advanced R programmer, can quickly dissect and analyze a dataset. In addition, to providing wrappers with error tracing, this package can be customized and extended to maintain a code base for creating predictive models when exploring multiple types datasets, consectively.
To summarize, self is good when analyzing lots data by yourself, but can also be extended by an organization to become a standard package for internal use.
When writing this package, the following guidelines were followed as strictly as possible, Google's R Style Guide
Checkout the web app housing-predictions that uses self and Shiny, a web application framework using R, to allow users to predict a value of a house in Cupertino, California, where Apple's headquarter (HQ) is located.
You can see a live demo of this application at:
I am currently a R&D Scrum Master/Project Lead for Mass Spectrometry Software Development at Agilent Technologies in Santa Clara, CA. I graduated with a Bachelor and Master of Science in Engineering from Johns Hopkins University. I have published several publications in the realm of Engineering and Biology.
I have deep interest in Data Science and its applications for Metabolomics, Genomics and Healthcare. In addition to my work and personal hobbies, I also teach developers and engineers in Silicon Valley Machine Learning using R. I enjoy teaching as it is a great way to meet myraid professionals from junior developers to senior directors in the valley and it is one of the best ways to learn.
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