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:
Comment
Vince,
We still see professed "data scientists" who insist that they are in fact better at providing insight and value because they know nothing about the data or domain that they are analyzing, making them "unbiased" in their assessments.
After working in "Data Science" for 20+ years, with extremely rare exceptions, I've never seen a person with no or limited domain expertise uncover are real insight, that the business found valuable. On the other, I've seen people who are very skilled, technically, make horrible mistakes in their analyses, that discredited the discipline in the eyes of business executives.
Absolutely love it... Domain Expertise, Experience, at times, is not considered important.. But they are vital....
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