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Goodbye, data science

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Re: Goodbye, data science

#241

Post by panax »

scottLobster wrote: post #156
_jx7j wrote: post #95

Earlier quoted context omitted.

>Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. One of the things I don't like about statements like this said in a Data Science context, is that they are true outside of Data Science as well. Executives make big decisions, managers make smaller decisions, nobody can evaluate how good/bad they really were for months or years. Engineers build…

Not to get too off topic, but as a 35 year old engineer it seems the world in general has far fewer consequences than I was raised to expect. Everything from businesses with bullshit ideas flourishing at a loss, to January 6 even being possible (politics aside I expected the Capitol Police to crack a lot more skulls than they did once people started smashing windows), to the whole FTX situation and the tepid response…

It might seem that way but it is hard to say with certainty but there is also probably sampling bias or declinism in that you are more likely to hear about negative events while normal or positive events are filtered out. And like PragmaticPulp said it can take time for things to catch up but they often do and people often eventually get what was coming to them.

Re: Goodbye, data science

#242

Post by zeroCalories »

I found the section on "Poor self-directed education" to be interesting. I've had similar feelings as a full stack developer, where in many ways I'm just learning APIs and other narrowly useful trivia. The only things I've had meaningful improvement in are stuff like soft skills, giving a good code review, or managing a complex project. Wondering what kind of skills someone like me should focus on to avoid the "embarrassing" skills/resume gap.

Re: Goodbye, data science

#243

Post by asdff »

dpkirchner wrote: post #41
Davertron wrote: post #36

Earlier quoted context omitted.

Well good luck then, in my experience the most free time I've ever had in my life was during college. I squandered massive amounts of that time doing things completely unrelated to education, and I definitely don't regret doing that. College isn't just about book learning after all. But still, BY FAR, college is the time of my life when I had the most free time to do whatever I wanted.

Middle and high school is where a lot of students learn to stop being curious due to a lack of time. College demands far fewer hours per day, but it can be hard to forget what was taught previously.

It depends on your courseload that semester. When I was taking organic chemistry I would spend a good 8 hours in the library a day monday-thursday on top of class, which would open the weekend up for partying. Wake up at 10 for class at 11, then straight to the library with the occasional break for meals or other classes until 11pm or so, whenever I got too tired to continue. By my senior year when I was just taking interesting electives, I was totally coasting, probably throwing in 2 hours a week in the library in total.

Re: Goodbye, data science

#244

Post by Davertron »

fn-mote wrote: post #144
dclowd9901 wrote: post #85

Earlier quoted context omitted.

That’s impressive. Between work (30 hours a week) and classes (full time credit load), I’ve never had less free time than when I was in college. And I’m speaking now as someone with 2 young kids and a full time job. Something tells me your experience is not commensurate with the standard college experience. Perhaps you didn’t have a full time job or only took part time credits?

> Something tells me your experience is not commensurate with the standard college experience. I know very few university students with significant work commitments. In the US, the stereotypical college student is not also holding down any kind of job. Maybe 5-7 hours of "work study" (light work running the reference desk at the library or working in the dining hall). Frankly, I doubt the majority could do learn a lo…

Yeah, this. I don't have any data to support this, but when I was in school, MOST people didn't have close to full-time jobs. I had a job where I probably worked 10 hours during the week at night and some full 8 hour shifts on the weekends. Most of the people I went to school with (and I would assume, maybe wrongly, that most people in better schools than I went to) didn't work AT ALL while they were in school, it was just those of us less than wealthy folk who actually had to work to have spending money and money to pay for books etc. I don't think my work load was overly demanding, but I was a Comp Sci major, fwiw.

Re: Goodbye, data science

#245

Post by asdff »

jrochkind1 wrote: post #191
nerpderp82 wrote: post #181

This blog post is directed at me, personally. Thanks W.D. This isn't just Data Science, I'd say that the gripes of the author are valid for about 60% of activity in tech companies. Not saying we can just eliminate 60% of it, but a lot of it supplies non-quantitative value that is driven by fashion (subset of politics) and direct politics. There are a lot of naked emperors walking around with lots of folks standing as…

Not just tech companies either. Go bigger. It's possibly 60% of activity in US white collar economy. (I can't speak for those who work with their bodies, could be or not).

Former body worker. 100% of us worked, because if you had nothing to do the boss will invent something for you to do or send you home.

Re: Goodbye, data science

#246

Post by BeetleB »

boringg wrote: post #28
jmhammond wrote: post #13

> But there’s also a part of me that’s just like, how can you not be curious? How can you write Python for 5 years of your life and never look at a bit of source code and try to understand how it works, why it was designed a certain way, and why a particular file in the repo is there? How can you fit a dozen regressions and not try to understand where those coefficients come from and the linear algebra behind it? I d…

To counter your professor opinion. The amount of extra time available as a student that I had to pursue things of interest was in the negative. All academic time was spent getting course content accomplished. I am a naturally curious individual but time limitations prevent further exploration in most circumstances. Additionally there is a relevancy factor weighed on top of it. If something looks curious I have to pre…

> The amount of extra time available as a student that I had to pursue things of interest was in the negative. All academic time was spent getting course content accomplished.

Did you go to a "good" school?

I went to a mediocre one for undergrad and a top school for grad. The one glaring difference I saw between the two: The top school's undergrad program gave students way, way too much busy work. All that work didn't give any insights, and was merely used to artificially distinguish students for grades. Their grad program was nothing like this.

Really glad I went to a mediocre school. Still learned everything, but had plenty of time to explore.

Re: Goodbye, data science

#247

Post by antipaul »

While the tone is a bit too negative (expected of someone leaving one place for “greener grass”), there are terrific points here that completely resonate with my own experience in data science - albeit not at a “median” place, but at a large non-Tech corp.

The biggest point I’d emphasize: “there is a general industry-wide need for people who are good at both data science and coding to oversee firms’ data science practices in a technical capacity.”

Even more, most of the data tech leaders I hear strongly suggest this is not possible without C-suite representation of a data/engineer expert (I’m talking about non-tech companies)

Finally, it’s actually not uncommon to see people shift from data science to data engineering due to similar motivations. It’s actually the technical leadership that is sometimes surprised at the shift. You hear about this in podcasts from DS/DE people.

Re: Goodbye, data science

#249

Post by antipaul »

scottLobster wrote: post #156
_jx7j wrote: post #95

Earlier quoted context omitted.

>Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. One of the things I don't like about statements like this said in a Data Science context, is that they are true outside of Data Science as well. Executives make big decisions, managers make smaller decisions, nobody can evaluate how good/bad they really were for months or years. Engineers build…

Not to get too off topic, but as a 35 year old engineer it seems the world in general has far fewer consequences than I was raised to expect. Everything from businesses with bullshit ideas flourishing at a loss, to January 6 even being possible (politics aside I expected the Capitol Police to crack a lot more skulls than they did once people started smashing windows), to the whole FTX situation and the tepid response…

Enlightening sociological reflection!

It seems that individuals often bemoan such a lack of consequences, but for some reason they are still quite prevalent in our “systems.”

I wonder how to harness the good intentions of individuals…

Re: Goodbye, data science

#250

Post by mjburgess »

wheelinsupial wrote: post #232
euix wrote: post #189

I have to agree with a lot of this - I started my career as a data scientist right out of a STEM PhD back when the term just started coming into existence. At the time, anyone who wanted to get hired as a Data Scientist needed to be trained as a professional scientist, i.e. have a PhD - at first my expectation that the purpose of my job was to apply the scientific method to solve business problems by leveraging the c…

> the higher up I climbed the more I realized the job had marginal business impact Do you have any observations why? I'm a pretty lowly business analyst, but my observation is if you don't own the decision making (usually by having profit and loss responsibility), you can't have much impact. Possibly it's the companies and industries I've worked at, but at the end of the day if the results don't meet expectations, it…

For the same reason science takes 100s (,1000s) of years to develop.

All the "intelligence" takes place in the humans that design experiments to collect unambiguous data. "data" absent a profoundly intelligent (, expensive, fraught, ...) experimental design is basically useless.

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