Hacker News Board Hacker News, browsed like a classic message board
Live data from Hacker News

Mathematicians becoming data scientists

quomodocumque.wordpress.com

Post reply on HN
Print view View original

21–30 of 122 posts

Re: Mathematicians becoming data scientists

#22

Post by jknoepfler »

M_Grey wrote: post #20
jknoepfler wrote: post #19

Earlier quoted context omitted.

I'm sorry, what? Being a good engineer in the real world means building quality solutions within time and cost constraints. It's a craft. One can be a good engineer and have both pride and curiosity. You also need humility and the ability to put your customer's needs ahead of your ego. I mean, by all means open up a little boutique data science consultancy that charges three orders of magnitude more for a solution th…

We're... not talking about engineers though.

A data scientist is an engineer.

Re: Mathematicians becoming data scientists

#23

Post by M_Grey »

jknoepfler wrote: post #22
M_Grey wrote: post #20

Earlier quoted context omitted.

We're... not talking about engineers though.

A data scientist is an engineer.

"Mathematicians becoming data scientists..."

At best you're talking about a career change, at worst you're talking about a watered-down version of your intended career.

Re: Mathematicians becoming data scientists

#24

Post by stared »

M_Grey wrote: post #7

•Can you walk away from a problem when the solution is “good enough,” are you able to switch between tasks or problems with relative ease? Are you OK with simple solutions to problems that could have more complicated solutions, but only with rapidly diminishing returns? Translation: Can you be a good assembly line worker, devoid of pride or curiosity and just do the damned job? If so, great, we can use you! If not, p…

Well, I went from academia (theoretical physics) to data science, and "good enough" solutions is exactly the thing I like the most.

That is - things that rather solve a concrete problem (given all constraints, including: dirty data, finite time, understanding the needs of clients) rather that have a pure and beautiful, but utterly useless for practical purposes, theorem to prove.

But sure, tastes do vary.

Re: Mathematicians becoming data scientists

#25

Post by pmiller2 »

diimdeep wrote: post #18

>. A big thing about the transition to tech is that you possibly start communicating with people who don’t really know what a vector space is. Be ready to have those conversations. Honestly ask yourself if you’re OK with having those conversations. This is so snobbish. What here is even think about ?

It's not snobbish at all. The CS/developer version is "someone who doesn't know what static typing is," or, maybe even "someone who doesn't know what computers are and are not capable of."

Re: Mathematicians becoming data scientists

#26

Post by cottonseed »

diimdeep wrote: post #18

>. A big thing about the transition to tech is that you possibly start communicating with people who don’t really know what a vector space is. Be ready to have those conversations. Honestly ask yourself if you’re OK with having those conversations. This is so snobbish. What here is even think about ?

This is the wrong comparison. Read the second footnote. An intern is a developer in training. The post is talking about mathematicians (who spent a decade or more learning a highly technical field) going to work in an environment where, largely, their peers, boss and consumers of their work do not know, have no interest in and and will never learn the most basic concepts of their field.

Re: Mathematicians becoming data scientists

#28

Post by laughfactory »

ecesena wrote: post #13
11thEarlOfMar wrote: post #10

I've been encouraging my daughter, a statistics major, to pursue data science by including Python/R in her studies and then possibly heading back for an MBA. But not sure if an MBA would be a benefit. Thoughts from actual data scientists?

Personally I think a PhD would be better than an MBA if she really wants to do data science, but generally speaking I'm for as more edu as possible, and I'm sure she'll benefit from an MBA too.

The PhD will only be useful in a small (but growing) subset of data science jobs. Their are data scientists who develop new algorithms and techniques, and those who apply the. For application, the PhD is probably overkill and extensive experience with a bachelor's, or a master's is better. For theory the PhD can't be beat, of course. I say the opportunity costs are high, and should be carefully weighed, because I have my bachelor's and am making more than a data scientist friend of mine who has his PhD. The vast majority of what he learned in the PhD program he hasn't found useful in the real world (yet, anyways). So it's all about what problems you want to work on. Personally I thrive in the application arena of data science, while others thrive in the R&D data science arena. If you like one you probably won't like the other.

Re: Mathematicians becoming data scientists

#29

Post by laughfactory »

M_Grey wrote: post #7

•Can you walk away from a problem when the solution is “good enough,” are you able to switch between tasks or problems with relative ease? Are you OK with simple solutions to problems that could have more complicated solutions, but only with rapidly diminishing returns? Translation: Can you be a good assembly line worker, devoid of pride or curiosity and just do the damned job? If so, great, we can use you! If not, p…

This summarizes real world data science really well, in my experience.

Re: Mathematicians becoming data scientists

#30

Post by ykler »

I was a PhD student in mathematical logic, and I know a couple people who got PhDs in this area and became data scientists. One thing about this field (and other areas of math) is that almost everyone in it feels mediocre. It's like 10% of the people are 10x better than the other 90%, 1% are 10x better than the next 9%, .1% are 10x better than the next .9%, etc. The top two people in the field seem to be notably better than everyone else. Another thing is that the problems are incredibly arcane. They have no connection to practical concerns or even often other parts of math, and also you can't really explain what you are working on to someone who works in a different area of mathematical logic (unless maybe the person is extremely good). So you have a lot of people getting PhDs who are very, very smart by any normal measure but who would be doing mediocre and arcane work in mathematical logic. Part of the appeal of working in data science or working for the NSA or something is that it is somewhat down-to-earth and people will think you are really smart even if you are at the bottom of the top .01%. (And you can do really great work at this level -- I don't mean to say it is all about caring what other people think.) It is a little weird that the blog post is so negative in a way.
Post reply on HN