only if "software engineer" is the new "system administrator"
Is “data scientist” the new “programmer”?
81–90 of 246 posts
Re: Is “data scientist” the new “programmer”?
#82Hacker News, finding new ways to make your job feel trivial since 2007.
Re: Is “data scientist” the new “programmer”?
#83Earlier quoted context omitted.
To be nitpicky, in the US, engineer means you graduated from an ABET accredited program in something like: Chemical engineering, mechanical engineering, civil engineering, electrical engineering, industrial engineering, computer engineering....etc. That is not to say programming isn't a difficult job that requires a lot of analytical and creative thinking similar to an engineer. The difference is in getting a degree…
> To be nitpicky, in the US, engineer means you graduated from an ABET accredited program in something like: Chemical engineering, mechanical engineering, civil engineering, electrical engineering, industrial engineering, computer engineering....etc. Do you happen to have a reference for this? At first glance, it seems to be incorrect rather than nitpicky. Anecdotally, I know plenty of people who do not have ABET acc…
https://motherboard.vice.com/en_us/article/vvapy4/man-fined-...
(There are lots of other articles about that case, that one sums it up mostly in the url)
Re: Is “data scientist” the new “programmer”?
#84data scientists write code for themselves while software developers write code for other people
Re: Is “data scientist” the new “programmer”?
#85Data Scientist has two terms in it : Data + Science. More often than not, people ignore the "Science" part of that equation.
That's an expression, not an equation. And the space between data and scientist quite obviously indicates that the two combine multiplicatively, not linearly as you have mistakenly written.
Re: Is “data scientist” the new “programmer”?
#86Deming, from Out of the Crisis (1986):
People with master's degrees in statistical theory accept
jobs in industry and government to work with computers. It is
a vicious cycle. Statisticians do not know what statistical
work is, and are satisfied to work with computers. People
that hire statisticians likewise have no knowledge about
statistical work, and somehow suppose that computers are the
answer. Statisticians and management thus misguide each other
and keep the vicious cycle rolling. (p. 133)
Re: Is “data scientist” the new “programmer”?
#87What I took from the post was not that a "data scientist" qualifies as a "programmer" in the modern sense, but in the sense of the kinds of things programmers did in the 1970s. And maybe he's expressing some nostalgia for those times.
I learned programming around 1982. I didn't pursue a programming career, but went to college and majored in math and physics. Today I often use programming in the way that a data scientist might, solving problems using high level tools. The data that I deal with are physical measurements. I'm not employed as a programmer.
I also work with a lot of programmers, so I get a glimpse of what they're doing, maintaining a million-line code base. And I have to admit that being thrust into that environment would have me waxing nostalgic about the good old days too. I'm happy doing what I'm doing, and happy that someone knows how to turn my stuff into production code if it ever gets to that point.
What I'm really doing is applying my domain knowledge in a domain that happens to depend heavily on computation. To answer Greenspun's question, what I'm doing is certainly more interesting -- to me. I have colleagues for whom wrestling with the monster code base, and the kinds of engineering it requires, are their source of fascination.
Re: Is “data scientist” the new “programmer”?
#88Data Scientist has two terms in it : Data + Science. More often than not, people ignore the "Science" part of that equation.
Someone said any field with "Science" in the name isn't really a science. Computer science, data science, political science, social science, etc. Physics, chemistry, biology don't have science in their name.
>> Someone said any field with "Science" in the name isn't really a science. Computer science, data science, political science, social science, etc
The etc. would also include cognitive science/neuroscience, medical science, earth science, material science, agricultural science, veterinary science, geoscience, food science, etc.
And of course as we all know climate science is fake./s
Generally when I hear a field with the word "Science" in the name I think of it as a more interdisciplinary field. Take Earth Science it draws on different areas of physics (ie wave physics), biology (ie ecology) and chemistry (ie kinetics). Earth science is still very much science it is just doesn't fit perfectly into the more foundational fields.
Re: Is “data scientist” the new “programmer”?
#89Earlier quoted context omitted.
Hah! At my company a decent proportion of engineers spend their lives scrambling to productionalize and operate the Lovecraftian concoctions of R and Python that our data scientists cook up on their laptops.
Where I work, the data scientists are more educated and experienced on containerization, CI tooling, unit testing, profiling tools, web service prototyping including API validation tools, caching layers, queues, GPU systems programming, etc. etc. We are constantly thwarted by infrastructure teams that use superficial policy basically so they can whine and complain that they don’t want to have to provide support for t…
I feel like you are roughly describing research programmers versus system administrators or operators in academic computing environments.
I think a big difference between research programmers and production/ops people is that as researchers we often chase a transient goal. Build some complex and horrible integration to compute a result or put something in a paper. We used to call these Rube Goldberg machines rather than Lovecraftian horrors, but we mean the same thing. Something that belongs on a movie set, with some Jacobs ladders arcing in the background. In some circles, it is called the heroic demo.
In the past, we might substitute other fads for your CI tooling or API validation tools. I remember when some research programmers were all-in on enterprise junk like J2EE/managed code, SOAP/WSDL, and other stovepipe tooling. There is a lot of cargo culting of such tools. When you have furnished your lab with rapid prototyping tools and focused on crazy integration stunts, you are almost always deluding yourself to think these tools are also giving you "production" system qualities.
Building something at the hairy edge of possibility is inherently about experimentation and risk-taking. Building reliable, production operations is inherently about conservative design and risk-mitigation. There seems to be a new cargo cult of devops which believes you somehow mash these together and the conflict disappears. You don't have to have to map the negotiation onto two teams with opposing objectives, but the negotiation has to live somewhere.
Magically erasing the negotiation just means that you have chosen to default on the optimization task and jettison concern for at least one of functionality, cost, or risk. Startups commonly do this because the VC funding has mitigated the risk elsewhere: you can fail because they've also funded your competitor who may succeed...
Re: Is “data scientist” the new “programmer”?
#90This is such a bizarre post. The reason why people use a language like R is because it is easy to learn and use (and install, via RStudio) for data analysis without having to be a well-trained programmer. I can’t recall ever hearing from anyone who has relied on R, doing so because it was computationally efficient. The point of the language is convenience — particularly with how easy it is to create attractive graphi…
A trend I've been noticing (especially as ML/AI tooling becomes more accessible) is that people believe the quality of data science code and workflows is proportionate to its complexity/LOC (since complex problems require complex code, right?). It's a toxic perspective that ignores recent and pragmatic innovations in the field.
I often see careers built more by complex non maintainable models that show fancy math than more simple ones.