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I don't try to keep myself updated. I don't listen to podcasts. I'm not subscribed to any data science newsletter. Neither do I go to meetups. I think this is a

by mo_42 2y ago

I don't try to keep myself updated. I don't listen to podcasts. I'm not subscribed to any data science newsletter. Neither do I go to meetups. I think this is all a distraction. If I encounter a problem that I cannot solve sufficiently with the methods I already know, I start exploring and read material until I find something that does the job. The other way around makes you try to apply your new and fancy method everywhere simply because you're excited about it and it's new. There's a similar phenomenon in tech in general, when people suddenly start to adopt OOP everywhere or there's a new JavaScript framework around the corner without assessing what the benefit will be.

staticautomatic 2y ago
I agree broadly, though I think it's important to distinguish between techniques, people, and religions. I'll follow certain people on LinkedIn who regularly post useful technical stuff in relatively plain language that I might not know about and which might come in handy. I've picked up some genuinely useful stuff this way. But then there are hordes of religious frequentists and bayesians having pseudo-intellectual knife fights and I avoid them about as vigilantly as I would people having actual knife fights.
noud 2y ago
I 100% do exactly the same. I gave up following what's new in Artificial Intelligence (Machine Learning?) years ago. 99% of it is distraction, and not worth my time to find that last 1% of useful information. Instead, I focus on improving my foundations: statistical inference, linear algebra, calculus, classical machine learning (e.g., regression, boosting, component analysis, ...), programming, domain knowledge, social skills, ... I only learn a new technique if I cannot solve it with my usual toolbox (which is not very often). I'm way more productive, have to work less hard, and I'm not distracted. Sure, I don't do that fancy new thing, but at the end of the day (or earlier) I get the job done. And I'm judged on what I do, and how it brings money into the company, not how I do it. Another benefit working mostly with a box of boring, old tools, is that it will likely still be relevant in the next 30 years. You never know how long that new popular thing will remain popular and useful. But I'm pretty sure we'll still fit datasets with linear/logistic regressions, optimize processes with linear programming, or do straightforward A/B testing for the next few decades (if not centuries or millennia).
- p1esk 2y ago
  Or, far more likely, you get laid off in 2 years because GPT-6 will do everything you can do, but better, faster, and cheaper.

by mo_42 2y ago

staticautomatic 2y ago

I agree broadly, though I think it's important to distinguish between techniques, people, and religions. I'll follow certain people on LinkedIn who regularly post useful technical stuff in relatively plain language that I might not know about and which might come in handy. I've picked up some genuinely useful stuff this way. But then there are hordes of religious frequentists and bayesians having pseudo-intellectual knife fights and I avoid them about as vigilantly as I would people having actual knife fights.

noud 2y ago

I 100% do exactly the same. I gave up following what's new in Artificial Intelligence (Machine Learning?) years ago. 99% of it is distraction, and not worth my time to find that last 1% of useful information. Instead, I focus on improving my foundations: statistical inference, linear algebra, calculus, classical machine learning (e.g., regression, boosting, component analysis, ...), programming, domain knowledge, social skills, ... I only learn a new technique if I cannot solve it with my usual toolbox (which is not very often). I'm way more productive, have to work less hard, and I'm not distracted. Sure, I don't do that fancy new thing, but at the end of the day (or earlier) I get the job done. And I'm judged on what I do, and how it brings money into the company, not how I do it. Another benefit working mostly with a box of boring, old tools, is that it will likely still be relevant in the next 30 years. You never know how long that new popular thing will remain popular and useful. But I'm pretty sure we'll still fit datasets with linear/logistic regressions, optimize processes with linear programming, or do straightforward A/B testing for the next few decades (if not centuries or millennia).

p1esk 2y ago
Or, far more likely, you get laid off in 2 years because GPT-6 will do everything you can do, but better, faster, and cheaper.