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The chief data scientist at my last job said a Data Scientist knows more programming than your average statistician and more statistics than your average programmer. There's this venn diagram he used to show with the intersection of skills for the different disciplines involved - some math, some engineering, some communications.

I think there's also an intersection with devops skills, maybe less important, but your hardcore statisticians usually put zero thought into operational considerations. Really the last bastion of "works on my machine" thinkers in the computing world. I just finished the Coursera "Reproducible Research" course and I was really struck how many of those principles parallel good software engineering practices - use source control, document through code, separate your environment from your code, automate as much as you possibly can, etc. I've been a software engineer for 20+ years but I want to get into data science partly because I've always been a data head, just without the theoretical background to do really interesting work, but also because I think I can bring some of the software engineering skills to bear.

Also, with grading peer's work on Coursera, I really realize that a lot of these candidates need help with their English and presentation skills. Many of the students put no work at all into the presentation, I imagine that's going to serve them poorly in the working world.

The way I've heard it from others in this forum, is that a DS is a combination of three jobs. They are analysts, in that they can work with data and squeeze insights out of it and they know enough about the business to know that the numbers mean and what differences matter. They are software developers, in that they can build actual software solutions to access and manipulate data, rather than relying purely on shake-n-bake existing tools. This helps them deal with very large data sets that are beyond conventional analyst tools such as spreadsheets. And finally they are experts in stats/ai/math who can build and evaluate sophisticated mathematical models.

It seems to me that's an awful lot to fit between one pair of ears.

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