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Ask HN: Was data science just hype?

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Re: Ask HN: Was data science just hype?

#71

I think people are realizing that data scientist without domain knowledge cannot create valuable insights. Enterprises seems to hire less data scientists actually, but they are trying to raise their employees' data skills. I think that's the cause of the growth of self-analytics tools. Below are examples of them. 1. Metatron Discovery : https://metatron.app 2. Metabase : https://metabase.com/

I would like to hear more about my European fellows w.r.t. how GDPR affected their ability to muster domain knowledge.

I used to work for a small start-up and the CTO was very strict on data access, making my life as feature developer and "data scientist wanna be" almost impossible.

He, on the other hand, had not only access to all data but also used the product as a consumer (which didn't make sense for ICs so we ended just playing with sales demo accounts). I ended leaving the company because of that.

Re: Ask HN: Was data science just hype?

#72
post #52
post #19

Earlier quoted context omitted.

I am wary when people wax lyrical about all of the ways they love using machine learning on data. It makes me nervous because i worry that they have a hammer and can't wait to use it on anything vaguely nail shaped.

ML makes predictions; testable predictions. Machine learning is an area where you need to be able to produce results. Fake it ‘til you make it isn’t going to cut it for long. Either these people produce something that works, or they don’t.

> ML makes predictions; testable predictions.

Well, no. ML solves the classification problem, not the prediction problem.

E.g.: The "is this a cat picture" problem is effectively solved, but we _still_ can't reliably predict something as primitive as a simple binary proportion.

Re: Ask HN: Was data science just hype?

#73
post #52
post #19

Earlier quoted context omitted.

I am wary when people wax lyrical about all of the ways they love using machine learning on data. It makes me nervous because i worry that they have a hammer and can't wait to use it on anything vaguely nail shaped.

ML makes predictions; testable predictions. Machine learning is an area where you need to be able to produce results. Fake it ‘til you make it isn’t going to cut it for long. Either these people produce something that works, or they don’t.

Yes and no.

You can certainly produce some plots and numbers, and possibly even plots and numbers that look good to your boss/clients/investors. The (multi)million dollar question is whether those numbers are actually meaningful. I think this is where a lot of ‘data science’, both in industry and academia, falls down.

Some state-of-the-art models don’t even generalize to test sets drawn from the same database, let alone similar data sources or the actual business problem. Unless you run a pet shop, telling breeds of dog apart, a la ImageNet, is probably not your goal.

Re: Ask HN: Was data science just hype?

#74

I think they are evolving to more specific roles. A couple of years ago some "Data Scientists" were actually doing Data Engineering. Now that distinction is more clear. You also have Machine Learning Engineers, who can help with deploying models or you can even see things like "Deep neural network engineer" or NLP data scientists.

What is the definition of “Engineer” in “Data Engineer” and “Machine Learning Engineer”? And what is the difference with “Scientist”.

I’m not from the US and in my country, “(civil) Engineer” is a legally protected title https://en.m.wikipedia.org/wiki/Civil_engineer#Belgium

Re: Ask HN: Was data science just hype?

#75

I think people are realizing that data scientist without domain knowledge cannot create valuable insights. Enterprises seems to hire less data scientists actually, but they are trying to raise their employees' data skills. I think that's the cause of the growth of self-analytics tools. Below are examples of them. 1. Metatron Discovery : https://metatron.app 2. Metabase : https://metabase.com/

I would like to hear more about my European fellows w.r.t. how GDPR affected their ability to muster domain knowledge. I used to work for a small start-up and the CTO was very strict on data access, making my life as feature developer and "data scientist wanna be" almost impossible. He, on the other hand, had not only access to all data but also used the product as a consumer (which didn't make sense for ICs so we en…

I’ve been in similar situation and it was really hard to be effective in product / high level planning meetings because it is easy to be blindsided. At the time, I was still an youn engineer in a big co, so I just thought it was my lack of technical experience, but in reality it was nothing technical to it but BS politics.

What boils down to is that people who have any extra data access privilege will have the lead.

Most of the insights will come from aggregate data, so I think companies could work around privacy concerns but I am no GDPR expert.

Back in my days in academia, there was a saying “if you have the trace, you have the paper”.

Re: Ask HN: Was data science just hype?

#76

Earlier quoted context omitted.

> My guess is that data science roles will merge with business analyst roles. Data Scientist is a buzz word for Statistician. Business Analyst is buzz word for Industrial Engineer. For example 10 years ago if you studied at my university you would witness that some Statistics students were doing second major mostly at Industrial Engineering and vice versa. They are already related for many years but average Joe has n…

>Data Scientist is a buzz word for Statistician It's really not. The skill set we need in terms of some software, system design, and a rich knowledge of modern data science libraries and trends is not something you should expect a statistician to have. Similarly, I certainly cannot prove asymptotic theorems like a statistician.

The only thing that they don't teach at Department of Statistics is Neural Networks.

Re: Ask HN: Was data science just hype?

#77
post #52

Earlier quoted context omitted.

ML makes predictions; testable predictions. Machine learning is an area where you need to be able to produce results. Fake it ‘til you make it isn’t going to cut it for long. Either these people produce something that works, or they don’t.

> Machine learning is an area where you need to be able to produce results. Having to produce results is one thing. Mindlessly throwing tensorflow/pytorch at problems is an entirely different problem. It's like those front-end devs who mindlessly insist that they need to use heavy javascript frameworks with convoluted build processes such as React/Angular to churn out a static web page with a couple of paragraphs and…

[deleted]

Re: Ask HN: Was data science just hype?

#78
post #52

Earlier quoted context omitted.

ML makes predictions; testable predictions. Machine learning is an area where you need to be able to produce results. Fake it ‘til you make it isn’t going to cut it for long. Either these people produce something that works, or they don’t.

> Machine learning is an area where you need to be able to produce results. Having to produce results is one thing. Mindlessly throwing tensorflow/pytorch at problems is an entirely different problem. It's like those front-end devs who mindlessly insist that they need to use heavy javascript frameworks with convoluted build processes such as React/Angular to churn out a static web page with a couple of paragraphs and…

You neglect the pointy-haired boss factor.

The tendency to pine for an ever-heavier minified, transpiled, inscrutable javascript blob, megabytes in size, also comes from bosses impressed by anyone displaying an aura of arrogance.

“Oh yeah? Well, I’mmm using [sparkle thingie] and it’s just sooo much better.”

That mode of thinking also comes from the top.

I once watched this turd of a middle manager lean on his underling, while sporting the biggest boss boner about basically nothing at all.

The mandate was that it's time to transpile everything under a new framework. One framework to rule them all.

So they get to this place where all the global variables are fucked, and it's a mess.

  Just put everything
  in namespaces, we NEED
  to move forward.
Then, blah blah blah components, blah blah blah modules, blah blah blah Bruce and Harriet Nyborg. Totally clueless to the idea that he might sound like a total fucking sham.

It's just hot air. None of it's real. There's blood in the water only in the sense that when the annual reviews come around, woah! Look out! 1% raise coming through! Finally. Minted as assistant manager at burger king.

It's just puffy egos in the king's court. No one is solving real problems. It's all boondoggles and exercise bikes at another bullshit job.

Here. Plug this wad of ad tech into this garbage news article template. Gotta be able to say it's "newer" and "better" too. Moths to a flame.

Pointy-haired boss perks up at all the talk of pointy, shiny objects.

  Well, I think it should 
  fluff my scrotum too, what 
  do you think?

  I concur.
Now the ad tech gets bundled with a boss scrotum fluffer.

Sprints go by. And nothing is getting done. Floundering, aimlessly adrift in high seas. The JS blob just gets bigger and dumber, but we march on. It must transpile. We have to be able to say we're hip. That we're "with it" or everyone will laugh at us.

The old way is disgusting. And under a new boss it stinks of "not invented here" syndrome. The old boss owned it, so it must be terrible.

Underlings scuttle like cockroaches at footsteps, terrified of boss and middle manager. Truly fucking dickless assholes. Spineless, and without souls. Non-player characters.

The demands are put forward. Begging is silenced. People are fired. Heads roll. It must transpile. We need to be able to say that we "do that" or people will think we're dinosaurs. (even though we are)

It's kind of a joke to witness a death march in a technical role. Mostly because, if the team is such a push over, to even entertain what is obviously a death march, they probably aren't smart enough to do their job.

So, when you see cargo cult JS floating around, it's fair to estimate it as a product of clucking trendy drones, and wimps getting trampled by boss thundercock.

Re: Ask HN: Was data science just hype?

#79

I think what companies really want is smart generalists with advanced math, programming, and modeling skills coupled with domain knowledge. That skill set will always carry high value in technical companies. The reason it carries value is the skills are difficult to acquire. I think the recent decline in interest reflects the rise of new data science candidates that are taking the path of least resistance to a career…

>I think what companies really want is smart generalists with advanced math, programming, and modeling skills coupled with domain knowledge. That skill set will always carry high value in technical companies.

What companies want is to be "in" on the data science hype, while they have no clue what they are doing and the most advanced "data science" they need are simple graphs, boxplots, and linear regressions.

Re: Ask HN: Was data science just hype?

#80

Was? As I always said it is Statistics. If you want "data scientist" employ Statisticians.

In my professional experience this is a little misguided. A pure PhD statistician isn't going to be able to hack it working on fast-paced production software environments and building end-to-end pipeline/software/ML systems. I mean, no doubt a PhD statistician could learn and be good at it, but the average statistician isn't geared up for this type of work.

On the other hand, your standard tech data scientist may find themselves out of their element if needing to design a very rigorous randomized trial for testing a new drug, and making careful inference (I mean I'm sure plenty could, but I'm not going to trust a 25 year old with two years work experience to do that).

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