I've been reflecting from time to time on the mystery of Hadley Wickham's "Readings in Applied Data Science" at Stanford:
https://github.com/hadley/stats337

The mystery is the only not-optional reading for the first week on "What the *&!% is data science?". It's a very short blog post with title "Data scientists mostly do arithmetic and that's a good thing": https://m.signalvnoise.com/data-scientists-mostly-just-do-arithmetic-and-that-s-a-good-thing-c6371885f7f6 . Why this short throwaway thing as the main reading, rather than, say, one of the optional readings like Donoho's big and thoughtful "50 Years of Data Analysis" (https://courses.csail.mit.edu/18.337/2015/docs/50YearsDataScience.pdf).

I think I understand it now. The question that the blog post raises is a deep one. If many data scientists are doing arithmetic, what the *&!% are courses on data science going to teach?

At the same time, I finally read "Data scientist: the sexist job of the 21st century" (https://hbr.org/2012/10/data-scientist-the-sexiest-job-of-the-21st-century).

The impression that I came away with, is that data scientists, in industry, are "data hackers", the data equivalent of the hacker movement in computing. They improvise, they invent, they build, they share.

Why did that happen now? Back to Hadley Wickham. It is because we have the tools now - particularly R and Python. It was technically possible to do this before, but it was too difficult and consuming of time and technical effort. The analyst was buried in technical problems, making to harder for them to think, and taking away mind space for working on new problems. These languagues have developed to the stage where they have made these problems vastly easier to solve and to explain. The result is an explosive growth in the range of tasks that data analysts can work on. Data scientists are the people who found these tools, and saw how they could apply them. Now let's work out what we should teach.

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