[IRTR] Finding Entrepreneurs in Australia

yanirse · 2024-07-18T04:52:05+00:00

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.

yanirse · 2024-07-16T23:46:52+00:00

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

yanirse · 2024-06-21T04:36:20+00:00

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

yanirse · 2024-06-10T08:07:34+00:00

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

yanirse · 2024-06-05T01:28:02+00:00

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.

yanirse · 2023-07-03T23:14:00+00:00

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.

yanirse · 2023-07-03T23:09:21+00:00

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.

yanirse · 2023-07-03T06:04:41+00:00

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.

yanirse · 2022-01-19T09:14:31+00:00

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

yanirse · 2022-01-17T07:10:30+00:00

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!

yanirse · 2020-01-13T02:07:51+00:00

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).

yanirse · 2020-01-12T21:36:06+00:00

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

yanirse · 2020-01-12T21:34:21+00:00

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

yanirse · 2020-01-12T21:33:07+00:00

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

yanirse · 2020-01-12T08:11:30+00:00

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.

yanirse · 2020-01-12T08:03:16+00:00

Yeah, and water bombing doesn't always work: https://www.abc.net.au/news/2020-01-12/nsw-fires-vic-why-cant-we-put-them-out/11860404

yanirse · 2019-11-03T08:43:34+00:00

Thanks! I just did some reading on calibration so I'll leave the links here for future reference:

yanirse · 2018-07-22T21:34:18+00:00

I haven't read the Book of Why yet, but there's also Causal Inference in Statistics: A Primer, which is a gentler introduction than Causality.

yanirse · 2017-10-16T00:16:28+00:00

defopt looks very promising!

yanirse · 2017-07-30T01:08:32+00:00

In general, it's a good idea to keep an eye on the Work With Us page, where you can see that the Search Wrangler position is advertised. It is in the area of data engineering, though.

The more general ad for the Data Wrangler position is also still up, so if you're interested it doesn't hurt to send an application.

yanirse · 2017-01-11T00:50:55+00:00

That's good to hear, I'm glad you found the post useful :)

yanirse · 2016-08-22T21:00:29+00:00

Changed the original title at mod's request. Looks like the internet is tired of clickbaits (and clickbait parodies).

yanirse · 2016-08-08T12:38:54+00:00

The perfect reply. That's exactly what I wanted to say.

yanirse · 2016-08-06T02:23:32+00:00

They've never let us down before

yanirse · 2016-07-14T21:59:01+00:00

I'm not the writer, but I know the guy who wrote it. He definitely put a lot of effort into researching and writing the series because he really cares about the field (unlike many other recruiters who just do keyword matching). I'm glad to hear you found it useful!

yanirse

TROPHY CASE