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Comment by tryitnow | Hacker News Reader

There's a recommendation I give to people who are writing their online personals ad: If you're sexy, there's no reason to say that you're sexy.

I think the same applies here.

A data scientist is a fancy way of saying a "statistician who can code (should be required in stats programs now anyhow) and who can communicate effectively"

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I'd be surprised if it was even possible to graduate in stats these days and not know R at least, and probably NumPy too.

This was ISyE, not stats, and it was 5 years ago, but I was amazed by how much extra work some people would do to avoid having to learn anything but Excel (meanwhile, I was messing around with R and whipping programs up to get better results in less time). This was at a highly ranked engineering program, too.

Based on a few people I've kept in touch with, it seems like it hasn't changed all that much at the undergrad level. The grad level was where the problem sizes and difficulty really forced you to use better tools.

In case anyone else is wondering, ISyE == Industrial and Systems Engineering.

At RIT at least, R and NumPy aren't in the core curriculum. Instead, there is a "Statistical Computing" class which covers SAS. Most students either use Minitab or Excel. Surprisingly (or not), a lot of in-class work is done using a graphing calculator. Of course, that also carries into a lot of the homework.

I wonder, do statisticians actually use graphing calculators to do stats?

I think of my job as essentially that of a statistician (data miner/data scientist/trader/research analyst/whatever you want to call it). I have never used a graphing calculator in my life. If I need to do a quick calculation I open a terminal and boot up R, Python or GHCi, depending on how I'm feeling and how complicated what I need to achieve is.

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