To those of you who may stumble upon this blog, I want to describe the purpose of it. First let me introduce myself. At the time of this writing, I am the director of manufacturing quality for a large company that makes solar panels (if you're interested here's my LinkedIn profile). I spent the first 7+ years of my career working for General Electric (GE) in various businesses while almost always working in manufacturing quality. This gave me a heavy dose of Six Sigma training and the statistics understanding that comes with it. Even before joining GE, while getting my masters degree in mechanical engineering, I was fascinated by the power of various methods of learning from data, especially design of experiments and optimization methods.
The more I have climbed the corporate ladder, the more I have realized that I miss the awe and excitement that comes from gleaning something important and powerful out of data that nobody could see before. There are times in my line of work, when I am able to use the data generated from the factory to create software/programs that help the organization "see" what is really happening, and help it react to the issues that really matter. Being able to create this type of understanding is limited by the types of data analysis techniques one knows. The more I learn about data mining and associated topics, the more I want to understand all of the data analysis options that are out there so I can pull the right tool from the toolbox when the time comes to analyze a particularly tricky data set. That is what this blog is about.
I plan to use this blog to document what I am learning as I take various online courses, read books, and do research on the subject of data mining. I plan to make my posts simple. Having read some of the papers and taken some classes on the subject already, there are plenty of PhD's out there that teach at the level of their understanding. It may be easy for them to understand, but it doesn't make it clear for the rest of us. This blog is going to be my personal solution for that problem. I hope it helps me...and you. ;)
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