This is another provocative KDNuggets blog post: the data scientist is reduced to three circles, missing the biggest, most important one that encompasses all three of them..
My answer: This Venn diagram misses the most important circle: domain expertise / business acumen. You can be a data scientist without computer science, statistics or data base (thought it would be very difficult). You can't be a data scientist without deep domain expertise and horizontal business knowledge.
Steven Miller wrote: I met Jeremy Howard formerly CEO of Kaggle back in October. He said that only one winner of a Kaggle competition was a domain expert. Perhaps domain expertise isn't what's needed at all because it creates bias that isn't easily overcome.
Here's my answer: Very few domain experts participate in Kaggle competitions, as they can make far more money leveraging their expertise on the job market, or by creating their company. Winning a Kaggle contest does not mean that you have created added value. Data science without sustainable added value is not data science.
I am an expert in online advertising, ad exchanges and fraud detection. Without some sort of real expertise developing successful solutions on real data, I would not be a data scientist. What makes me a data scientist is this experience, and of course it involves (big) data.
Knowing linear regression, clustering algorithms, time series, R, Perl, SQL, data base architecture is not what makes me a data scientist. Besides those skills are easy to acquire - plenty of tutorials are available online. Your value and real knowledge is stuff that is not found online, for free. Otherwise, you could be replaced by a professional in Africa or Eastern Europe for a fraction of the cost, or your task could get automated and produced by a machine (I'm actually working on data science automation).
Three examples where domain expertise is critical: