
Computer Science Loses to Math in New Hiring Formula - mattmcknight
http://online.wsj.com/article/SB10001424052702304871704575160553254798886.html?mod=WSJ_hpp_sections_tech
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tokenadult
Statistics is a very interesting subject, and it is a distinct subject from
mathematics proper. Here (in what is becoming a FAQ post for HN) are two
favorite recommendations for free Web-based resources on what statistics is as
a discipline, both of which recommend good textbooks for follow-up study:

"Advice to Mathematics Teachers on Evaluating Introductory Statistics
Textbooks" by Robert W. Hayden

<http://statland.org/MyPapers/MAAFIXED.PDF>

"The Introductory Statistics Course: A Ptolemaic Curriculum?" by George W.
Cobb

[http://repositories.cdlib.org/cgi/viewcontent.cgi?article=10...](http://repositories.cdlib.org/cgi/viewcontent.cgi?article=1002&context=uclastat/cts/tise)

Both are excellent introductions to what statistics is as a discipline and how
it is related to, but distinct from, mathematics.

A very good list of statistics textbooks appears here:

[http://web.mac.com/mrmathman/MrMathMan/New_Teacher_Resources...](http://web.mac.com/mrmathman/MrMathMan/New_Teacher_Resources.html)

I have encountered some recent examples of people finding interesting work by
being able to step into the intersection of computer science and statistics,
with a strong pure math background besides. A lot of statistical researchers
need programming help, and a lot of programmers need to be more aware of
statistical issues. It's a win-win when a learner learns a lot about math,
statistics, and computer science.

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eru
I like your comment. Let me add a second view-point to your first sentence.

> Statistics is a very interesting subject, and it is a distinct subject from
> mathematics proper.

Mathematics proper is more than one subject, already. (Theoretical) computer
science and statistics are just two more branches of math.

The notion of statistics as being outside math remains me a bit of how
thermodynamics was viewed as not proper physics (or prober chemistry) in the
19th century.

The study of thermodynamics got a huge boost from the commercial need to
understand and build better steam engines. If you look close enough,
thermodynamics is actually statistics. (And of course statistics and
probability theory is just applied measure and integration theory.)

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dagw
I kind of agree that statistics should be viewed as slightly different subject
from math proper. The math aspect of statistics is not, in itself, the
important aspect. The interesting (and in my opinion harder) aspect is the
more subjective part of getting a good feel for how to pick from the myriad of
statistical tests available to use on your data and to analyse exactly what
the results from those tests mean relative to your data.

Both of those aspects are core to applying statistics and neither are purely
mathematical. Both are also often ignored or marginalized by those who simply
see statistics as applied analysis-light.

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yummyfajitas
Actually, picking tests is a purely mathematical topic. The only reason it
feels like an art and not math is because people still use frequentist
statistics.

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eru
Picking an optimality criterion for your tests is an art. Then picking optimal
tests is pure math. But for anything beyond toy problems picking an optimal
test (or optimal parameters for your test), so there comes the black art part
in again.

What do you pronounce as the alternative to frequentist statistics? May I
guess you'd pick Bayesian approaches?

Mathematical things like maximum likelihood estimators can be seen as solving
a min-max problem atop of Bayesian calculations.

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ryanjmo
>>>>Employees teach new recruits to use tools for comparing the performance of
one version of a feature with another and how to determine what sort of
difference in response is _meaningful_.>>>>>

I hate when people use statistical significance to determine what is
_meaningful_. People wrongfully think you need 95% confidence in the data to
make a decision and move forward with a feature change, etc. For me when I am
changing things on my site based on data mining, I will make a decision with a
60% confidence (which can come within 5-10 samples in most cases). Hacking
websites is engineering not science! Overall it makes sense to trade a low
confidence value for speed of development.

~~~
_delirium
Statistical significance is also not even a sufficient condition for a
difference to be meaningful. In the real world, very few things are truly
_identical_ , so you can eventually get a statistically significant difference
for just about any comparison, given enough samples. But the differences may
be negligibly small.

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MichaelGG
Meta: When I clicked the link from HN (or from the WSJ front page), I got 2
paragraphs and a paywall. But using Google on a line of the text sets it up so
you get the article.

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barrkel
Firefox extension RefControl, to change the referrer for wsj.com to
<http://www.google.com> , seems to work well.

RefControl:

<https://addons.mozilla.org/en-US/firefox/addon/953>

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DeadlyDave
I would be fascinated to hear comparisons of the performance of graduates of
different disciplines as developers. I know we everyone can think of a few
examples of good and bad for all; but it would be interesting to collate large
numbers of peer reviews and see if any disciplines stand out. In the eight
companies I have worked for, I think it's quite likely the maths graduates
stand out. If not maths then probably electrical engineering. The chemistry
and physics graduates are widely distributed. I know a lot of computer science
graduates but I would hesitate to categorise any as great developers. A
hundred or so is a terribly small sample so I would love to see results from a
decent sample. (It happens I am about to start recruiting again, and no, I'm
not a maths graduate.)

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jey
Why? The difference in skill between programmers is huge, but only a tiny
amount of that variance could be predicted by the programmer's major. This is
as misguided as discarding everyone who doesn't have a 4.0 GPA or didn't go to
a top-10 school.

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nandemo
I suspect the average math undergrad is not as good a programmer as the
average CS undergrad (but my sample is restricted to my college).

However, if we count only the maths majors who end up working as programmers
then I believe there will be some significant self-selection. So if such a
study were done I wouldn't be surprised if math and physics majors were found
to be better than CS majors.

In any case I'd agree with you that in that I would never blindly use that in
a hiring process.

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eru
Combine this with the ascend of compressed sensing--which leverages e.g.
linear optimization--and I am set.

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raffi
I recommend reading programming collective intelligence if you want to learn
some data mining techniques that you can use out of the box, today.

