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Ask HN: Was data science just hype?

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Re: Ask HN: Was data science just hype?

#51

Post by Gene_Parmesan »

darkhorn wrote: post #39
bradleyjg wrote: post #23

My guess is that data science roles will merge with business analyst roles. Python and r will slowly join excel as tools of choice for making tables and charts to stick in powerpoint slides and pdf reports. Meanwhile the machine learning side of things will be the domain of _something_ engineers with candidates more likely to come from the computer science/math/engineer world rather than the sciences. (Other then tho…

> 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…

I still don't really know what a business analyst role truly entails. In my office they seem more like mini project managers w/o the management. They talk to internal stakeholders a bunch, handle a lot of our UAT, and I guess do some reports stuff? One has moderate tech skills (but no programming) and the other is just really good at Excel.

I'm still not sure what their actual formal responsibilities are.

Re: Ask HN: Was data science just hype?

#52

Post by mr_toad »

Ntrails wrote: post #19
tictacttoe wrote: post #6

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 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.

Re: Ask HN: Was data science just hype?

#53

Post by kthejoker2 »

Gene_Parmesan wrote: post #51
darkhorn wrote: post #39

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…

I still don't really know what a business analyst role truly entails. In my office they seem more like mini project managers w/o the management. They talk to internal stakeholders a bunch, handle a lot of our UAT, and I guess do some reports stuff? One has moderate tech skills (but no programming) and the other is just really good at Excel. I'm still not sure what their actual formal responsibilities are.

I mean, a business analyst should do just that: analyzes the business. It's usually their job to translate strategic areas of improvement in the business unit into specific outcomes.

They ask questions to identify areas of improvement; translate those into functional (and sometimes technical) requirements for other areas (not just IT) to fulfill; and then coordinate the efforts to implement those requirements, potentially as PMs, product owners, Scrum masters, UAT leads, or just a SME.

The best BAs (paraphrasing the data science JD) know more tech than the business and more business than the tech.

Re: Ask HN: Was data science just hype?

#54

Post by dual_basis »

kevinventullo wrote: post #50
blihp wrote: post #28

It wasn't 'just' hype, but it was over-hyped. There are companies that have their act together from a data standpoint and can make use of data scientists, whatever that term actually means in the context of their organization, but most can't. So the companies who spun up a data science initiative but had no business doing so are now likely saying things like 'what do you mean we don't have the necessary data?' and 'w…

If the data is a mess, cleaning it up is very much legitimate data science work in my opinion.

Sometimes it can't be cleaned up, or the process of cleaning it up takes too long or is more expensive than the company wants to spend. Sometimes the clean up process is error prone, or leaves you with too little useable data. Sometimes the data really is too noisy and no amount of clean up is really possible. It's definitely true that data clean up is a problem data science can address, but it's not a magic wand.

Re: Ask HN: Was data science just hype?

#55

Post by lumost »

PLenz wrote: post #26
ddragon wrote: post #14

"AI" (or whatever rebranding it gets) always works in cycles, with a phase of excitement and overpromising followed by a phase of apparent underdelivering and skepticism. But what actually happens is that the innovations just become part of the normal tooling, and stop being called "AI". At some point there is no need to hire a "data scientist", as any python programmer is already expected to know how to use numpy, p…

The value is a "Data Scientist" isn't that they know how to use a tool - it's that tell know why to use _that_ tool (technique) and not this other one.

Always be wary of people who's skill is telling others what to do. You may find the people they're telling already know.

Re: Ask HN: Was data science just hype?

#56

Post by blihp »

kevinventullo wrote: post #50
blihp wrote: post #28

It wasn't 'just' hype, but it was over-hyped. There are companies that have their act together from a data standpoint and can make use of data scientists, whatever that term actually means in the context of their organization, but most can't. So the companies who spun up a data science initiative but had no business doing so are now likely saying things like 'what do you mean we don't have the necessary data?' and 'w…

If the data is a mess, cleaning it up is very much legitimate data science work in my opinion.

It's absolutely part of the process... just not something companies always realize they're signing up for, or the degree to which they are, when they initiate projects. One problem is that the cost of cleaning it up can easily exceed the cost of, and benefit from, the work they planned on doing with the data. Another is that sometimes it's a mess (structurally or the company failed to record key data at point of collection that can't be determined after the fact etc.) to the point of being useless in terms of being able to glean meaningful insight from it.

Several of the Data Warehousing projects I've dealt with could be better described as Data Landfills. One can't just dump data into a hole for years, let it rot, and expect goodness when you go back to look at it.

Re: Ask HN: Was data science just hype?

#57

Post by xs83 »

It wasn't hype, however people got very confused about what they actually needed vs what they thought they wanted.

When someone says they want a "Data Scientist" what they really mean is "I want a Data Scientist who is also a Data Engineer".

I have seen so many companies spend a really decent chunk of money on a data scientist and then are shocked to find that this data scientist doesn't know how to deploy models, set up spark clusters or know how many and what type of GPU they need to use to get the job done.

After all - that is not their purpose.

We were in a similar situation, but what we needed was a Data Engineer - we had a rough idea of where we wanted to go and what we wanted to achieve, he was doing a Masters in Data Science so he had that background as context.

We will look at adding a Data Scientist to our ranks in the future - but they will be working side by side with a Data Engineer who can action their requirements!

Re: Ask HN: Was data science just hype?

#58

Post by redstone08 »

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/

Re: Ask HN: Was data science just hype?

#59

Post by geezerjay »

mr_toad wrote: post #52
Ntrails wrote: 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.

> 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 images.

Re: Ask HN: Was data science just hype?

#60

Post by huac »

bravura wrote: post #12
foolrush wrote: post #11

Earlier quoted context omitted.

It’s an epistemology / ontology question, as folks familiar with the humanities would spot in little time. Aka it’s not “data” until something empowers the created metric a meaning. The map is not the territory. https://www.amazon.com/Raw-Data-Oxymoron-Infrastructures/dp/...

I know what all those words mean, I've studied critical theory, and I have no idea what your point is.

The map and the territory are pretty basic terms, right? There's a Wikipedia article on it, even: https://en.wikipedia.org/wiki/Map%E2%80%93territory_relation
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