Those data scientists strong in statistics are likely to

"develop new statistical theories for big data, that even traditional statisticians are not aware of. They are expert in statistical modeling, experimental design, sampling, clustering, data reduction, confidence intervals, testing, modeling, predictive modeling and other related techniques."A quote that particularly jumped out at me:

"Just like there are a few categories of statisticians (biostatisticians, statisticians, econometricians, operations research specialists, actuaries) or business analysts (marketing-oriented, product-oriented, finance-oriented, etc.) we have different categories of data scientists."I was once ranting to a CS a colleague of mine, "data science is a thing, but 'data scientist' is not." The justification being that we are a collection of (in no particular order) software engineers, statisticians, mathematicians, economists, etc.. He quickly pointed out to me that those who may have referred to themselves as computer scientists around the middle of the 20th century were likely scoffed at as they were really collections of logicians, mathematicians, electrical engineers, and so on.

Another reminder to never assume things will stay the way they are.

And don't forget our very own DSDC quantified these categories in a book. I don't see the point in specializing an already specialized field.

ReplyDeletehttp://www.amazon.com/Analyzing-Analyzers-Harlan-Harris-ebook/dp/B00DBHTE56

Yes! Thank you, Majid. Harlan, Sean, and Mark's work has heavily influenced my thinking. And it helped me when I was on the job market. If I was interviewing for a data scientist position, I was sure to define what "kind" of data scientist I was early in the interview process.

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