Jan de Leeuw owns the Internet

One of the best things to happen on the Internet recently is that Jan de Leeuw has decided to own the Twitter/Facebook universe. If you do not already, you should be following him. Among his many accomplishments, he founded the Department of Statistics at UCLA (my alma mater), which is currently thriving. On the occasion of the Department’s 10th birthday, there was a small celebration, and I recall Don Ylvisaker mentioning that the reason they invited Jan to UCLA way back when was because he “knew everyone and knew everything”. Pretty accurate description, in my opinion.

Jan’s been tweeting quite a bit of late, but recently had this gem:

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I’m not sure what Jan’s thinking behind the first tweet was, but I think many in statistics would consider it a “good thing” to be a minor subfield of data science. Why get involved in that messy thing called data science where people are going wild with data in an unprincipled manner?

This is a situation where I think there is a large disconnect between what “should be” and what “is reality”. What should be is that statistics should include the field of data science. Honestly, that would be beneficial to the field of statistics and would allow us to provide a home to many people who don’t necessarily have one (primarily, people working not he border between two fields). Nate Silver made reference to this in his keynote address to the Joint Statistical Meetings last year when he said data science was just a fancy term for statistics.

The reality though is the opposite. Statistics has chosen to limit itself to a few areas, such as inference, as Jan mentions, and to willfully ignore other important aspects of data science as “not statistics”. This is unfortunate, I think, because unlike many in the field of statistics, I believe data science is here to stay. The reason is because statistics has decided not to fill the spaces that have been created by the increasing complexity of modern data analysis. The needs of modern data analyses (reproducibility, computing on large datasets, data preprocessing/cleaning) didn’t fall into the usual statistics curriculum, and so they were ignored. In my view, data science is about stringing together many different tools for many different purposes into an analytic whole. Traditional statistical modeling is a part of this (often a small part), but statistical thinking plays a role in all of it.

Statisticians should take on the challenge of data science and own it. We may not be successful in doing so, but we certainly won’t be if we don’t try.

 
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