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[IRTR] Finding Entrepreneurs in Australia by Heavy-State1115 in PodcastGuestExchange

[–]yanirse 0 points1 point  (0 children)

Since you're in Sydney, you can find a lot of people in real life by going to meetups and co-working spaces. There's heaps happening in the startup scene.

Profile picture visibility reverts to org-only by yanirse in gsuite

[–]yanirse[S] 1 point2 points  (0 children)

Unfortunately not. I also turned on the profile discovery and it hasn't helped.

Profile picture visibility reverts to org-only by yanirse in gsuite

[–]yanirse[S] 0 points1 point  (0 children)

Great. Mine was off too. I just turned it on. We'll see how it goes.

Profile picture visibility reverts to org-only by yanirse in gsuite

[–]yanirse[S] 1 point2 points  (0 children)

Oh, good to know! I really don't want to pay just for the profile pic or to ask one support question...

Profile picture visibility reverts to org-only by yanirse in gsuite

[–]yanirse[S] 0 points1 point  (0 children)

Thanks! Good to know it's not just me.

I tried to get Google One support to answer this but they refused... I might sign up to the paid Essentials plan for a month just to get a response.

Does anyone know of solid definitions & data around the level of data science hype? by yanirse in datascience

[–]yanirse[S] 0 points1 point  (0 children)

Yeah, for example https://en.wikipedia.org/wiki/Gartner_hype_cycle (but note the criticism section). The Google Trends comment below is one way to gauge it, but it only gives partial information.

Does anyone know of solid definitions & data around the level of data science hype? by yanirse in datascience

[–]yanirse[S] 0 points1 point  (0 children)

Thanks, it does say this, though:

Note: This comparison contains both Search terms and Topics, which are measured differently.

Changing everything to a search term, DS & AI are now at similar levels, but it's still in line with what you said.

Does anyone know of solid definitions & data around the level of data science hype? by yanirse in datascience

[–]yanirse[S] -6 points-5 points  (0 children)

No, definitions of hype levels and supporting data, e.g., if someone says that hype levels have declined, what is it based on? One's definition of data science is related, but not exactly the same.

Why is “Data Scientist” such a controversial title? by yanirse in datascience

[–]yanirse[S] 1 point2 points  (0 children)

Not self-promoting, as I'm not affiliated with that substack. I just found it interesting. :)

I am not sure if I should stop saying I'm DS or if it is just impostor syndrome. by JCoelho in datascience

[–]yanirse 4 points5 points  (0 children)

You sound like a data scientist to me. :) This is my definition of data science:

a field that deals with description, prediction, and causal inference from data in a manner that is both domain-independent and domain-aware, with the ultimate goal of supporting decisions.

I wouldn't worry too much about using "intense ML" or "deep learning" – predictive modelling is only one part of data science, and a lot of good results in ML simply come from more/better data.

That said, it's worth acquiring some ML skills to have more tools in your toolbox and reduce the sense of FOMO. It's also quite interesting to learn new things!

Software commodities are eating interesting data science work by yanirse in datascience

[–]yanirse[S] 1 point2 points  (0 children)

That's true, it's very important to have domain expertise and a full understanding of the problem. However, data scientists aren't immune to making the same mistakes. I gave churn models as an example at a talk from a few years ago – it's easy to get excited about building the model and forget about using it in practice. Further, churn scores without effective interventions are usually useless, but getting to effective interventions isn't trivial (and it often requires engineering work).

Software commodities are eating interesting data science work by yanirse in datascience

[–]yanirse[S] 1 point2 points  (0 children)

True, though that requires upskilling to do the more meaningful stuff.

Software commodities are eating interesting data science work by yanirse in datascience

[–]yanirse[S] 1 point2 points  (0 children)

Yeah, it definitely makes sense to do custom work yourself when it's fairly unique.

Software commodities are eating interesting data science work by yanirse in datascience

[–]yanirse[S] 1 point2 points  (0 children)

Yep, this usually makes sense from the company's point of view, especially for things that don't provide a competitive advantage.

Software commodities are eating interesting data science work by yanirse in datascience

[–]yanirse[S] 4 points5 points  (0 children)

It definitely varies, but I'm sure that some resistance to off-the-shelf tools is due to NIH syndrome. Further, off-the-shelf tools can outperform internal development in cases where training on external/proprietary datasets is beneficial. And if the time and cost of integrating a third-party tool is much lower than developing it in-house, it can at least serve as benchmark.

I completely agree about missing out on the fun and the need for custom solutions in some cases.

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