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12 data science mistakes to avoid

AI, machine learning and analytics aren’t just the latest buzzwords; organizations large and small are looking at AI tools and services in hopes of improving business processes, customer support and decision making with big data, predictive analytics and automated algorithmic systems.

IDC predicts that 75 percent of enterprise and ISV developers will use AI or machine learning in at least one of their applications in 2018. But expertise in data science isn’t nearly as widespread as the interest in using data to make decisions and improve results. If your business is just getting started with data science, here are some common mistakes that you’ll want to avoid making.

Source: cio.com
Author: Mary Branscombe

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