181–190 of 415 posts
There are a lot of naked emperors walking around with lots of folks standing as close as they can to shield them from the cool winter wind.
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>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…
In real life there's basically one absolute goal, and that's survival. And that's largely assured in developed western countries these days, unless you do something really stupid. Everything else is socially constructed, and pretty arbitrary. There are some decisions that are fairly consequential for what your life will look like (where & whether to go to college, what field to go in, what metro area to move to, which employers to work for, who to marry, whether & when & with whom to have kids), but you will still have a life regardless, it just might be a slightly smaller house or a spouse that you click with worse or less disposable income for travel.
That's also instructive for what decisions actually do matter. Don't do drugs. Wear your seatbelt. Don't get pregnant unless you mean to. Don't play with loaded guns. If you're staying away from major causes of death you're generally doing pretty well.
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I become wary any time someone utters the phrase, "show me the data" or any variation there of. There is a specific type of leader who thinks that within the data lurks a magical solution just waiting to be discovered. There is also the leader who uses data as a trump card to win arguments and these folks are perhaps even worse. This is not new. The origination of the phrase, "lies, damned lies, and statistics," can…
I have only heard “show me the data” when someone wants someone else to support a claim. I do not see why this would necessarily be a bad thing.
A typical VP will have an MBA and maybe took statistics in high school.
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My title is still software engineer, but I effectively do data engineering, and I work closely with data scientists. I love a lot of it, but there's still plenty of bullshit to deal with. Just in the technical side, dealing with Python is a perpetual gong show, and most of my team's work seems to revolve around configuration of secrets and K8s. I'm fortunate to be the guy that nerds out about performant code, so when…
You wrote: > What does this mean?
> 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…
Earlier quoted context omitted.
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…
Nope, nope and nope again. I refute this utterly, as a teaching academic. Contact hours at most universities are around 2-4 hours per week per 15-credit module. To gain a degree, you have to take 120 credits a year, typically two terms of 4 x 15 credit modules, or 8-16 hours of contact per week maximum with the entire summer off. You therefore have at least 24 hours a week to study on your own to bring your working w…
The difference probably belies in the rigor of the program. It sounds like you are working in a non-engineering based program. In our engineering programs we had 40 hours of class time + lab time per week.
I had a concurrent arts degree at the same time which is was, in comparison, incredibly light workload - though concurrently it took time away.
The only time that I will say was much lighter was in the final year of undergrad - the course load finally lightened up.
N.B. this whole conversation clearly excludes summer.
This hit all the same high notes I was feeling when I quit Data Science to become a software engineer. It's an infinitely better gig and I encourage all my colleagues with enough chops to make the same switch.
How did you do the transition from DS to SWE?
I've been a data scientist for quite awhile now at many different places, but every time I start interviewing again I always make sure to include a few pure software engineer roles in the positions I'm interviewing for. Even for some pretty elite teams, I'm still able to get to the final rounds but so far have always realized I still personally prefer the DS roles I'm looking at.
Any data scientist who wants to keep working on quantitative problems in the future should aim to be a solid software engineer.
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…
But over time, the higher up I climbed the more I realized the job had marginal business impact. Usually a big company would hire a bunch of PhDs with fancy degrees and stick them in some "Advance Analysis" department and leverage them as internal consultants, which just meant creating some models, writing a powerpoint deck, get a pat on the back from the execs - not a single model would ever see the day of light. I got all the way up to Director this way, before calling it quits this January, at the end I had basically nothing to do except work on "corporate AI strategy", which meant writing presentations and white papers for upper management.
It was comparatively easy job, one could coast their entire life in some of these corporations - especially in government sanctioned oligopolies like banking.
Earlier quoted context omitted.
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…
I think you hit the nail on the head there with the survivorship bias and the raised in a bubble comments. Most people are raised in a bubble because children generally can't cope with how messy and complicated the world is. And systems and companies that last a long time can point to how successful they were because of their good decisions while ignoring their equally bad decisions that really should have undone the…
I agree that human systems have always been fragile, but have long been papered-over by things like "decency", "tradition" and "doing the right thing" and in extreme cases, mobs with pitch-forks.
I disagree that it won't change in our lifetime(s) - the extreme polarization and tribal politics will get worse and people will let systems break - or intentionally break systems just so that their team will gain a short-term win. I have no idea what new horror it will take to remind people to be decent to each other again, but looking back at how divisive COVID-19 was, I'm not hopeful.