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Ask HN: Am I too late for the “Data Science” wave?

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Re: Ask HN: Am I too late for the “Data Science” wave?

#141

Post by hnracer »

surething123 wrote: post #121
hnracer wrote: post #102

Earlier quoted context omitted.

SQL is almost never used in algorithmic trading by data scientists. Learn Python, it is used universally. Don't learn R.

Why not learn R? In the last year I've spent around 80% of my time working with R, coming from the last five years almost exclusively with Python, and there are some great reasons to use R. Although if the tidyverse didn't exist I'm not sure I'd be saying that. I find that suite of packages together to be a very cohesive set of tools for doing data science.

Three reasons (I know both languages well): (1) R is used much less in the data science industry, and (2) Python is a more universally useful language. If he learns it for data science then he can easily write utility scripts, build a back-end, etc. (3) the overlap between R and Python capabilities is so significant it would be a waste to start with R, I would only suggest picking it up if he needs some niche package that he can't get in python.

Re: Ask HN: Am I too late for the “Data Science” wave?

#142

Post by thom »

giardini wrote: post #135
thom wrote: post #123

Genuinely shocked by this question. There is no part of the data science stack that is in any way settled. Even more broadly I don’t think statistical best practice is at all established, or at least well distributed. You haven’t even missed the boat in terms of being able to make money off raw buzzwords and zero skills. At the very basic technical level, there’s infinite work to be done optimising machine learning s…

And hope that boat is not the Golgafrinchan Ark Fleet Ship B : https://hitchhikers.fandom.com/wiki/Golgafrinchan_Ark_Fleet_...

And yet, what would we give for more telephone sanitizers right now?

Re: Ask HN: Am I too late for the “Data Science” wave?

#144

Post by d0mine »

ghaff wrote: post #132
d0mine wrote: post #107

Earlier quoted context omitted.

There is a book "Range: Why Generalists Triumph in a Specialized World" which claims there are domain-specific problems that are more likely to be solved by people originally outside that domain.

I don't doubt there are examples where a fresh set of eyes and lack of knowledge about what can and can't be done can break out from "the way we've always done things." But it's probably not the way to bet in the general case.

The book claims that "generalist" are the rule at least if we look at the very top of certain fields e.g., Nobel laureates.

Re: Ask HN: Am I too late for the “Data Science” wave?

#145

Post by markus_zhang »

deng wrote: post #51
jimbob45 wrote: post #29

Earlier quoted context omitted.

> There is a big oversupply of people for the junior-mid level data science jobs. Honest question is there any field where that isn’t true right now? Seems like there are no junior jobs in any sector.

German here. From my perspective it's hard to find - embedded systems programmers, meaning people who know real-time systems, have good C/C++ knowledge, know their way around the Linux kernel and are also able to do basic things with a scope. - good(!) C++ programmers in general - people who know devops and software development infrastructure C++ might not be sexy, but there's a vast amount of legacy software out the…

I think for C++ programmers either you have to pay a premium and hire senior people in their 40s (like 60s and 70s for COBOL), or find someone who is willing to learn and have the patience to get him on road not now, but 3-6 months from now.

I feel that it's rare that companies choose the second road so usually they pay a premium.

Re: Ask HN: Am I too late for the “Data Science” wave?

#146

Post by junon »

dehrmann wrote: post #113
junon wrote: post #61

Earlier quoted context omitted.

As a software engineer who has worked intimately with data scientists in Big Tech companies, this comment sums it up exactly. One minor thing to add is that not only is the data science field overflowing with junior/mid candidates, but the entire computer science field as a whole.

What happens in seven years when those candidates become experienced? Will they have dropped out? Will there be a glut of experienced software developers?

I sincerely doubt a large portion of them are cut-out for the sector at all. That's the issue.

Re: Ask HN: Am I too late for the “Data Science” wave?

#147

Post by jeffreygoesto »

dmurray wrote: post #95
deng wrote: post #84

Earlier quoted context omitted.

I agree. Another thing to consider is portability. You might have to port your software to obscure platforms with bad C++ compilers where compiler bugs are not uncommon at all, so it's better to stay clear of more advanced features. Also, you might have a legacy system which does not have modern C++ compilers, so you might have to restrict yourself to C++03. Another thing is code size, so templates should be used jud…

These replies both don't seem like they describe someone with "excellent" C++ skills. Sounds more like someone who knows a lot of advanced features of the language, but doesn't make the correct tradeoffs considering portability and/or performance. Maybe it could be that he didn't know all the requirements up front and his mediocre colleague accidentally wrote code that better suited the company's unstated requirement…

Ah, ok. In my head I do have a distinction between excellent C++ and excellent domain skills. So that person had excellent C++ skills but did not properly choose which C++ subset to use for the domain. From your comment I think you would say "excellent" implies "in all relevant aspects", whereas I just meant the pure programming language. What I tried to write in my first comment is that it is more and more difficult to find both combined in one person.

Re: Ask HN: Am I too late for the “Data Science” wave?

#148

Post by CinchWrench »

6gvONxR4sf7o wrote: post #118
CinchWrench wrote: post #96

Earlier quoted context omitted.

>the fundamental skills that you need are mathematics and software engineering So much this. If I have to interview another junior-level DS who has a MNIST project in their github and still somehow can't manage fizzbuzz or a fibonacci function I'm probably going to take up religious asceticism. EDIT: I said junior, but I meant Senior. We're talking people with PhD's who claim to have done extensive software engineeri…

If I have to interview another whose only ML tools are GLMs, random forests and boosted trees (only ever with one hot encoding, of course) I’m going to do the same.

Those and SVR's get me through 99% of the algorithmic part of my job, though!

The rest is some unsupervised stuff like k-means and PCA.

What would you like to see instead? (INB4 CNNs/RNNs other deep learning topics)

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