5 Machine Learning Books Worth Reading

Machine learning remains a hot topic as organizations attempt to squeeze insights and competitive advantage from their data sets. I’ve selected five books that may be of interest if you are embarking on a machine learning initiative or career. Obviously, there are much more great books out there and I’d like to hear your recommendations—recommendation […]
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Tackling a new dataset

With all the excitement (hype?) surrounding machine learning, there’s a tendency for decision makers (and many analysts) to believe that insights are just waiting to spring forth from the data when they are finally released by these magical algorithms. When it’s time to sit down and actually work with a real dataset, disillusionment quickly sets […]
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Version control for data scientists using Git and RStudio

Look at the working directory of the average data science project and you’ll see things like this: cust-churn.csv cust-churn.R cust-churn-good.zip cust-churn-old.csv cust-churn-progess-meeting.R cust-churn-working.R cust-churn-working2.R cust-churn-20160217.R test.R test-bk.R Every time a change needs to be made, files are copied to preserve the working code. As changes will often be made to multiple files, it’s common to […]
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The Importance of Data Analytics

The current obsession with predictive analytics and big data has many organizations fixated on “bottom-up” analysis. Hype has resulted in business leaders thinking there are huge nuggets to be mined from their data lakes—if only they can find the right tools. Time and time again, I see this mindset result in disappointment and disillusionment with […]
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Charts are Bad — Less UI, More UX

In my work as a decision science oriented software developer, I often explain to my clients that what they really want is a big green button that, when pressed, gives them the answer to the question they are currently wrestling with—a Magic 8-Ball that dispenses specific, data-driven advice. The challenge is, how close I can […]
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