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Goodbye, data science

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Re: Goodbye, data science

#401

Post by ozim »

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…

I would not agree with general apathy. I get it more as reality. Strict standards are BS. If you get up sober in the morning and go to work it is like 80% of what is expected from an adult.

Re: Goodbye, data science

#402

Post by xxpor »

time_to_smile wrote: post #282
xxpor wrote: post #271

Earlier quoted context omitted.

>They certainly couldn't implement most things from scratch if they needed to (neither could most data scientists). Could most CS folks actually implement Linux or Chromium from scratch?

I'm clearly talking about quantitative modeling tools. That said, while Linux and Chromium are massive projects each with years of development with thousands of engineers behind them, so of course it would be ridiculous to expect a single engineer to build such a thing. I also wouldn't expect an MLE to build SKLearn entirely as is from scratch on their own. However, I do certainly hope most CS folks could implement a…

>However, I do certainly hope most CS folks could implement an OS or Web browser from scratch.

I am way, way less optimistic than you then.

I doubt even 10% of CS grads, let alone people who have been out of school for a few years, could tell you what a page table is.

Re: Goodbye, data science

#403

Post by fritzo »

ChrisMarshallNY wrote: post #155

I have no axe to grind, irt data science/engineering, as I have no experience in either. However, it seems this person's biggest gripe is with good old crap management; the bane of business for hundreds of years. This line stood out: > Companies all over were consistently pursuing things that could be reasoned about a priori as being insane ideas– ideas any decently smart person should know wouldn’t work before they’…

This resonates. I wish it didn't. I'm experimenting with ever more delicate communications to avoid spoiling the soup.

Re: Goodbye, data science

#404

Post by throwaway2037 »

andrewnc wrote: post #219
throwaway2037 wrote: post #134

Earlier quoted context omitted.

You wrote: > What does this mean?

I think "The Gong Show" was an old tv show about amateur talents. Sometimes good, most of the time terrible and hilariously unaware. Not sure if that was what was intended here.

So is the GP criticizing Python? If yes, I am curious to know why. No, I am not here to defend Python. The constant runtime exceptions due to typing mistakes is so tiring.

Re: Goodbye, data science

#405

Post by disgruntledphd2 »

grepLeigh wrote: post #252

I'm reading a lot of sour grapes here, but I'd like to offer a more optimistic future snapshot (if you're a data scientist and happen to be reading). I agree that many companies hire data scientists with only a vague idea about how to utilize them, but the same is true of software people in general. "Software is eating the world" and so is the practice of extracting value from data. The margin on software is high - o…

> the demand for senior leadership FAR outstrips the available supply.

This is so very, very true. Most of the "bad" data science orgs I've spent time with, are bad because leadership is either bluffers or a data engineer/BI type person. It's generally hard for these types to run effective DS orgs as the skills needed are very, very different.

Re: Goodbye, data science

#406

Post by disgruntledphd2 »

laichzeit0 wrote: post #172
lajosbacs wrote: post #111

Earlier quoted context omitted.

I too am critical of data science, but I think that this is a bit unfair. The scientific part could be called just 'statistics'.

Should we call deep learning “statistics”? It’s mostly empirical. Should recommendation systems be called “statistics”? What about multi objective non convex optimization? Not everyone is doing regression and classification all day. The above were studied in the Computer Science curriculum at my university. I work with a statistician, like someone with a degree in mathematical statistics. They know nothing about any…

> Not everyone is doing regression and classification all day.

Yeah, some of them are doing unsupervised learning (recommender systems) too!

I dunno, I personally think we'd all have been better off if we'd called it statistics as at least then people would realise that the field wasn't created yesterday.

Re: Goodbye, data science

#407

Post by disgruntledphd2 »

frgtpsswrdlame wrote: post #7

Read this yesterday and absolutely loved it. Especially feel the pain regarding working with management. I think there's an accountability that comes with evaluating management decisions with data that nobody really wants. I still have a lot of half-formed thoughts/opinions about this but it really feels like data-driven requires strict discipline but the data people who would be accountable for that discipline both…

Just as an FWIW, I've been interviewing data people for about a decade now, and I would definitely be far more impressed with the simpler approach, but I realise that people like me are a minority in the field.

Re: Goodbye, data science

#408

Post by samuell »

> Personally, I’ve benefited a ton from reading the first couple chapters out of advanced textbooks (while ignoring the last 75% of the textbook)

Anecdotally, this is something I've thought about many times, that an awful lot of books cover like 80% of the important stuff in the first say 4-5 chapters, and then are just full of filler.

I find this extremely frustrating as I'm at the same time fearing to miss out on some important insight in the latter chapters, while reading the full length of most books is simply super hard to do in any sensible quantity.

Otherwise, great post. Going through a similar transition, for some of the same reasons :)

Re: Goodbye, data science

#409

Post by NeutralCrane »

whatever1 wrote: post #397
bart_spoon wrote: post #392

Earlier quoted context omitted.

The goal posts are only moved if they were in the incorrect position in the first place. Statistics isn’t the science of prediction, it’s the science of uncertainty management. There is always uncertainty, and how well you’ve measured uncertainty only can be accurately assessed over a large enough time frame over a large enough number of events. It’s like when people got upset about Trump winning when 538 only gave h…

Whoa, slow down with your elitism. Who do you think you are?

What elitism?

Re: Goodbye, data science

#410

Post by Calavar »

justsocrateasin wrote: post #102
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.

Yeah I hardcore disagree with this. Partly my fault for saying yes too much, partly my work schedule, partly being in a weed out program that really worked you to the bone. Some semesters I was doing like 70-80 hours a week on average, split between managing clubs, homework, attending class, working part time jobs, studying. One week I remember being busy from 7am to 2am for 6 days straight. a few semesters I had a l…

Interesting. I had a very different experience. Double major, working in two labs simultaneously, active member of local ACM, interned with local startup during the school year, volunteered at a local soup kitchen. All that together was about 50 hours/week. Academics (including homework, studying, etc) was only 25 hrs/week on average. But I was very fortunate to have the advantage of not needing to work, which gave me the freedom to scale back my hours on a particularly busy week.

I learned a lot from my CS classes, but I actually felt like most of the value from the degree came from overhearing random chitchat between professors or other students and the reading more about those ideas and experimenting with them in my free time.

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