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Computer Science Loses to Math in New Hiring Formula

26 points by mattmcknight 17y ago

MichaelGG 17y ago
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.
- barrkel 17y ago
  Firefox extension RefControl, to change the referrer for wsj.com to http://www.google.com http://www.google.com , seems to work well. RefControl: https://addons.mozilla.org/en-US/firefox/addon/953 https://addons.mozilla.org/en-US/firefox/addon/953
tokenadult 17y ago
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 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=1002&context=uclastat/cts/tise http://repositories.cdlib.org/cgi/viewcontent.cgi?article=10... 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.html http://web.mac.com/mrmathman/MrMathMan/New_Teacher_Resources... 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.
- eru 17y ago
  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.)
  - bitdiddle 17y ago
    reminds me of a comment a classmate used to make (we were taking a course together on measure theory, taught in the statistics dept. which was separate from our math dept.); "Inside every hard statistics problem is a trivial analysis problem struggling to get out"
    - eru 17y ago
      Our teaching assistent for probability theory gave us real hard nuts to crack. And he was strict about the rigour of our answer--we often had to ground our proofs in measure theory to get past his radar. And he allowed no handwaiving. The demands were a bit annoying at the time, but I credit him with getting a understanding of probability theory. I wish every course would have been like this.
  - dagw 17y ago
    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.
DeadlyDave 17y ago
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.)
- ssp 17y ago
  (It happens I am about to start recruiting again, and no, I'm not a maths graduate.) Out of curiosity, are you an EE?
  - DeadlyDave 17y ago
    I never did manage to get a job with my chemistry degree, and now I am decades out of date. With the small amount of programming I do these days I'm probably out of date with that now too. :-( I have only worked with two chemistry majors that I know of - and they were wildly different to each other. I am a fan of peer reviews as opposed to what managers think is going on - so I would personally not discard people's opinions about who the best developers in their group were. I can easily think of a list of developers at the places I've worked who would have been regarded as outstanding - and I believe every developer working at those places would have about the same list.
- jey 17y ago
  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.
  - deleted 17y ago
deleted 17y ago
[deleted]
ryanjmo 17y ago
>>>>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 17y ago
  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.
eru 17y ago
Combine this with the ascend of compressed sensing--which leverages e.g. linear optimization--and I am set.
raffi 17y ago
I recommend reading programming collective intelligence if you want to learn some data mining techniques that you can use out of the box, today.

26 points by mattmcknight 17y ago

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.

barrkel 17y ago
Firefox extension RefControl, to change the referrer for wsj.com to http://www.google.com http://www.google.com , seems to work well. RefControl: https://addons.mozilla.org/en-US/firefox/addon/953 https://addons.mozilla.org/en-US/firefox/addon/953

tokenadult 17y ago

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 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=1002&context=uclastat/cts/tise http://repositories.cdlib.org/cgi/viewcontent.cgi?article=10... 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.html http://web.mac.com/mrmathman/MrMathMan/New_Teacher_Resources... 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.

eru 17y ago
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.)
- bitdiddle 17y ago
  reminds me of a comment a classmate used to make (we were taking a course together on measure theory, taught in the statistics dept. which was separate from our math dept.); "Inside every hard statistics problem is a trivial analysis problem struggling to get out"
  - eru 17y ago
    Our teaching assistent for probability theory gave us real hard nuts to crack. And he was strict about the rigour of our answer--we often had to ground our proofs in measure theory to get past his radar. And he allowed no handwaiving. The demands were a bit annoying at the time, but I credit him with getting a understanding of probability theory. I wish every course would have been like this.
- dagw 17y ago
  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.

DeadlyDave 17y ago

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.)

ssp 17y ago
(It happens I am about to start recruiting again, and no, I'm not a maths graduate.) Out of curiosity, are you an EE?
- DeadlyDave 17y ago
  I never did manage to get a job with my chemistry degree, and now I am decades out of date. With the small amount of programming I do these days I'm probably out of date with that now too. :-( I have only worked with two chemistry majors that I know of - and they were wildly different to each other. I am a fan of peer reviews as opposed to what managers think is going on - so I would personally not discard people's opinions about who the best developers in their group were. I can easily think of a list of developers at the places I've worked who would have been regarded as outstanding - and I believe every developer working at those places would have about the same list.
jey 17y ago
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.
- deleted 17y ago

deleted 17y ago

[deleted]

ryanjmo 17y ago

>>>>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 17y ago
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.

eru 17y ago

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

raffi 17y ago

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