Hadley Wickham probably.

Also Andrew Gelman

Florence Nightangle, maybe? Her data viz is really impressive, like her 'coxcomb' diagram on mortality in the army.:

https://www.theguardian.com/news/datablog/2010/aug/13/floren...

> She was also a pioneer in the graphical presentation of data. At a time when research reports were only beginning to include tables, Nightingale was using bar and pie charts, which were colour coded to highlight key points (eg, high mortality rates under certain conditions). Nightingale was keen not only to get the science right but also to make it comprehensible to lay people, especially the politicians and senior civil servants who made and administered the laws.

https://ebn.bmj.com/content/4/3/68.full

Nate Silver of 538

Hadley Wickham, Andrew Gelman, and maybe Pete Warden are who data scientists look up to, but I think Nate Silver is the first data scientist to be a household name (admittedly, NYT-reading upper middle class households, but still).

That's a correct description of 'data science' as it is understood around me, among social scientists.

Gelman, Tufte and Wickham are in the pantheon, sometimes followed by dataviz fellows like Cairo.

Never heard of Warden.

Silver would likely be described as a 'data journalist' rather than (or at least as often as) a data scientist.

Efron, Hastie, Tibshirani (basically Stanford stats).

Don't forget David Donoho. His paper "50 years of data science" provides a great historical context of data science and a vision of what "Greater Data Science" should be.

https://courses.csail.mit.edu/18.337/2015/docs/50YearsDataSc...

Also Tukey, Chambers, Cleveland (all three from Bell Labs), Breiman, Tufte.

I would also submit Judea Pearl to the list.

They are statisticians.

They wrote the books Elements of Statistical Learning and Introduction to Statistical Learning in R. Those books are about least squares regression, clustering, decision trees, random forests, boosting, additive models, support vector machines, etc.

All these are common statistical learning methods used in Data Science.

Also, if you read the fantastic computer age statistical inference from Efron & Hastie (it's available online!), you notice they are both fans of data-science! The whole book reads like a big argument why we need data-science and why traditional statistics is not always the answer.

This comes especially obvious in the epilogue, where they try to give a quick oververview how the concept of a "data-science" formed and how statistics diverged into data-science + ML and the traditional statistics-community.

They end the book by arguing that both communities should find to each other, because fundamentally they try to do similiar things. I also think is badly needed. Unfortuntaly I experience some of arrogance on both sides, which makes it harder! (DS/ML-people have no idea what they are doing and only throw their algorithms on problems & benchmark them! Statisticians are obsolete & i can just automate them with NNs!)

Wouldn't you consider AI / ML to be a specialization of data science?

> Lampord

Lamport, in case anyone's googling :)