This is a great rant.
A lot of good engineers stand by the rule "use boring technology". Data scientists should adopt the rule "use boring math".
Goodbye, data science
311–320 of 415 posts
Re: Goodbye, data science
#312I've had similar experiences to the author and fellow HNers in this thread.
I just wanted to add that my personal transition from DS to DE has allowed me to work with a wider variety of data. DS is mostly tabular; data out in the wild comes in all shapes and sizes, and learning different techniques has been interesting.
Re: Goodbye, data science
#313Earlier 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…
no, warehouse workers, nurses, other people with extreme work hours requirements and tons of metrics, are constantly being fired for failing to meet quota or whatever.
its the "the higher the pay the easier the job" paradox.
Re: Goodbye, data science
#314See a lot of pessimism here, and heard a lot of the same stuff by grumpy engineers over my career "nobody knows how to do statistics properly", "scientists are bad at coding" and "managers don't care about quality and rigour". I'm old enough to say it all pre-dates data science as a term. It all comes across as a conspiracy like everyone is bad on purpose.
There is a null hypothesis here, that the average person in role x is just average at that role. It is an extraordinary person who has high level skills across multiple domains like maths/science and coding, maybe so extraordinary that they wouldn't be working with you...
I'll admit that the article rings true, but I think there is an implied intentionality that I don't agree with. We are all just plodding along, doing our best with limited information and skills.
Re: Goodbye, data science
#315This 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…
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Re: Goodbye, data science
#316Earlier quoted context omitted.
Data scientist is much more catch-all from what I've seen. But a lot of that varies a lot by geography too (for example in the US people very often use DS very differently from how the title is used in the UK).
>in the US people very often use DS very differently from how the title is used in the UK I haven't heard about this before and now I'm curious- can you elaborate on the differences?
In the US it's more common for data scientist to be similar to a product analyst or a data analyst perhaps with better technical skills. In the UK data scientist is more likely to be someone who is doing applied ML work (other titles for this are ML engineer or applied data scientist).
Obviously it's not a perfectly clean separation but it's a trend, and people sometimes end up really talking past each other. You can see on r/datascience which is very US-heavy how people often recommend to beginners not to bother with advanced ML, stick to SQL, basic Python and analytics, and in the UK data science job market that's outright bad advice (it's fine advice for the UK analytics market which is a separate thing).
Re: Goodbye, data science
#317Earlier 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…
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.
Not my experience at all. Proper studying takes a lot of intense work, far more than I've ever needed to put in in my post-graduation working life.
Re: Goodbye, data science
#318> Nobody knew or even cared what the difference was between good and bad data science work. Meaning you could absolutely suck at your job or be incredible at it and you’d get nearly the same regards in either case. In my experience it's even a little bit worse than that. Approaches that are wrong from a statistics point of view are more likely to generate impressive seeming results. But the flaws are often subtle. A…
>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…
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Re: Goodbye, data science
#319Earlier quoted context omitted.
> Consequences often catch up slowly. Agreed. Few people defy gravity; in the end most hit the ground. The phrase “slowly, then suddenly” comes to mind.
Should term consequences rarely align with expectations. But Long term consequences always do.
The universe is eventually consistent
Re: Goodbye, data science
#320Unfortunately it seemed pretty clear from the start that this is what data science would turn into. Data science effectively rebranded statistics but removed the requirement of deep statistical knowledge to allow people to get by with a cursory understanding of how to get some python library to spit out a result. For research and analysis data scientists must have a strong understanding of underlying statistical theo…
That answer somehow reminds me of an article in logicmag: An Interview with an Anonymous Data Scientist [1].
[1]: https://logicmag.io/intelligence/interview-with-an-anonymous...