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By Thomas Maydon

We’ve covered a few fundamentals and pitfalls of data analytics in our past blog posts. In this blog post, we focus on the four types of data analytics we encounter in data science: Descriptive, Diagnostic, Predictive and Prescriptive.

When I talk to young analysts entering our world of data science, I often ask them what they think is data scientist’s most important skill.  Their answers have been quite varied.

My message to them is that their most important skill will be their ability to translate data into insights that are clear and meaningful to a non-quant. The Swedish statistician Hans Rosling is famous for this. It’s an often overlooked skill. The following TedTalk by Hans Rosling sheds some light: