This past Friday I resigned from my position as a Civil Engineering Manager at SYSTRA, my employer of the last 6+ years. I did this because an opportunity of a lifetime knocked on my door, an opportunity that will give me a chance to pursue my passion in an exciting and growing field. In short, an opportunity to follow my dreams.
I’ve accepted a position as a senior consultant at RapidMiner, in their Boston headquarters, and I couldn’t be more excited about this. Not only will I be leveraging my engineering, predictive analytics, presentation/teaching, and Rapidminer skills, but I will be empowering organizations to utilize and adopt Rapidminer in ways they’ve never dreamed of.
If you asked me in 2007, when I started this blog, that my passion for data analytics and RapidMiner would eventually lead to a career with them, I would’ve laughed at you! Now, I’m reporting for duty the first week of March.
When my last box is packed and I turn off the light at my old office for the last time, I will be placing a new bet, one that will pay off in a big way. I will be putting all my chips in, going long on Rapidminer, and not looking back!
Why? Because I believe in RapidMiner. I believe that it can empower its users and their organizations to find new nuggets of information in their data that could change the world. I want to be a part of that, something larger than myself. I want to be a part of a community of thinkers, doers, and practitioners where ideas lead to new innovations and a better world. Who wouldn’t want to be a part of that?
My dear friends, I owe all this to you! To my readers, commenters, tweeple, Ralf (for meeting me in NYC long ago), Ingo (for inviting me to RCOMM 2010), the Dortmund RapidMiner team, and all the RapidMiner users that reached out to me over the years with comments, questions, and friendship, I owe all of this to you. From the bottom of my heart, thank you!
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Learn RapidMiner and Data Science
I used RapidMiner to do a lot of this time series analysis. If you want to learn more about how to use RapidMiner, I recommend the following books. I know the authors and they do a great job of breaking down how to use RapidMiner and apply it to the concepts of Data Science for your projects.