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Data scientists are the brand-name salesmen of tech. They talk about "data science" so much that every business owner is now convinced that they need to buy int

Data scientists are the brand-name salesmen of tech. They talk about "data science" so much that every business owner is now convinced that they need to buy into machine learning and "Big data" to be legitimate in the current year, but despite all of the chatter it isn't any clearer what exactly "data science" is or how it's different from technology and methods that have existed for decades.

baron_harkonnen 7y ago
Whenever people make this comment I have to seriously question if they've ever worked with any actually data scientists or at a company doing any data related work at scale? Data science is a unique skill set that is quite different than any previously existing role (most of which, for the most part, still exist). "Big data" is not some myth. I've worked at companies big and small that are easily ingesting terabytes of useable data per day and queries consume multiple days worth of this semi-structured data. So starting out you need people with the basic software proficiency to efficiently query and work with this data. Virtually all data science team meetings that I've been in have assumed a pretty strong familiarity with probability/statistics, linear algebra, and calculus. White board solutions that come up everyday involve a non-trivial amount of quantitative reasoning. It's incredibly rare to see non-DS people with these skills outside of what were traditionally research roles in larger companies. Likewise the job requires not just knowing the math but knowing how to implement it when necessary. Sure there are plenty of black box libraries out there, but these will only get you so far. Diving into the software implementation details of these things is a pretty common issue. What decades old methodologies do you think are being merely rebranded in the data scientists you've worked with? Nearly all of the pure stats people I've worked with in the years past did most of their work on the white board (or SAS) but left the code up to someone else. Business analysts still exist and honestly have none of the technical skills necessary. Software engineers rarely have the quantitive background to quickly prototype. In all the places I've worked DS is a genuinely unique intersection of skills that requires a pretty reasonable proficiency in across the board to be effective.
- disgruntledphd2 7y ago
  I think that both of you are right, to be honest. There have always been stats people that code, and coding people that do stats. Most of the useful (for structured-data problems) models are pretty old, and you can solve an awful lot of business problems with linear models with carefully chosen features. (it is perhaps worth pointing out, that with the exception of ReLU, CNN's in their modern form have existed since the 80s). But on the other hand, the large-scale hiring of people to do this work is pretty new, as well as the volume of data available to even moderately sized services, so there's a quantitative change that is somewhat qualitative. I'm also interested in the fact that there is no mention of A-B tests in the OP, given that the need for this is relatively new, and something that probably pushed a lot of companies towards needing more quantitative people, which led to the current nature of "modern" data science.
minimaxir 7y ago
As a current data scientist, the problem is that the concepts behind data science/big data are legitimate solutions to business problems, but the current marketing of the field is either misleading (e.g. it's not all about building models/AI, and I wrote a post complaining about that: https://minimaxir.com/2018/10/data-science-protips/ https://minimaxir.com/2018/10/data-science-protips/ ), or it's business upsell for an enterprise product that may not be the optimal solution to a given problem. It's one of the reasons I've switched more to publishing open-source tooling for data science/AI instead of blog posts about data science.
- bunderbunder 7y ago
  The obsession with "AI" is particularly fascinating. 10 years ago, before it was a household term, people could mention without blushing that their production implementation ended up being a simple if-statement. If anything, being able to come up with an insight that was clear and well-focused enough to be implemented in such a straightforward way was a feather in one's cap. Nowadays, if you aren't building a model with at least a few million parameters (though billions would be better), that ends up being so complex that the only hope for productionizing it is to stick it into a container and ask the engineering and ops folks not to peek inside it too carefully - sort of like a parent leaving the door to their teenager's bedroom closed - then nobody wants to take it seriously. Call me a curmudgeon, but it doesn't half remind me of a while back when I was being directed to process 12MB datasets on a distributed computing cluster. At some point the tools stopped being a means to an end, and started being the only thing anybody cares about.
  - deleted 7y ago
    [deleted]
  - salty_biscuits 7y ago
    It is funny, what you are usually supposed to be replacing /automating with ML is the nasty 100 line conditional statement that forms some bit of "business logic" that nobody really understands. This thing usually being the time pressured fever dream of some dev that left the company years ago. Now it is replaced by something that is even more incomprehensible. If it is done well then at least you get performance guarantees and the ability to retrain. If it is done badly, then you don't know what the features are, which model belongs with which dataset, how to regenerate training sets, where the original training set went, how to set the hyperparameters to get reasonable training performance, etc, etc.
usgroup 7y ago
I’ve been reading about “expert systems” of late for a particular gig I’m pitching for ... most the literature is 1980-1985 and what I realised was is that expert systems were all the rage back then. The IBMs of this world sold them and you clearly didn’t know what’s best for you if you weren’t buying them. Is there anyone sufficiently old to remember AI in the 80s reading this? What was the job title du jour back then ?
mr_toad 7y ago
When the term was first coined it had a clear meaning - someone who was a statistician, and a programmer, and a subject matter expert. Someone who could, for instance, write code from scratch to calculate maximum likelihood estimates, and use it to make predictions on real world data. The term got coopted by snake oil salesmen pretty soon after.
- bigred100 7y ago
  Wait now he has to write his own optimization routines too?

baron_harkonnen 7y ago

Whenever people make this comment I have to seriously question if they've ever worked with any actually data scientists or at a company doing any data related work at scale? Data science is a unique skill set that is quite different than any previously existing role (most of which, for the most part, still exist). "Big data" is not some myth. I've worked at companies big and small that are easily ingesting terabytes of useable data per day and queries consume multiple days worth of this semi-structured data. So starting out you need people with the basic software proficiency to efficiently query and work with this data. Virtually all data science team meetings that I've been in have assumed a pretty strong familiarity with probability/statistics, linear algebra, and calculus. White board solutions that come up everyday involve a non-trivial amount of quantitative reasoning. It's incredibly rare to see non-DS people with these skills outside of what were traditionally research roles in larger companies. Likewise the job requires not just knowing the math but knowing how to implement it when necessary. Sure there are plenty of black box libraries out there, but these will only get you so far. Diving into the software implementation details of these things is a pretty common issue. What decades old methodologies do you think are being merely rebranded in the data scientists you've worked with? Nearly all of the pure stats people I've worked with in the years past did most of their work on the white board (or SAS) but left the code up to someone else. Business analysts still exist and honestly have none of the technical skills necessary. Software engineers rarely have the quantitive background to quickly prototype. In all the places I've worked DS is a genuinely unique intersection of skills that requires a pretty reasonable proficiency in across the board to be effective.

disgruntledphd2 7y ago
I think that both of you are right, to be honest. There have always been stats people that code, and coding people that do stats. Most of the useful (for structured-data problems) models are pretty old, and you can solve an awful lot of business problems with linear models with carefully chosen features. (it is perhaps worth pointing out, that with the exception of ReLU, CNN's in their modern form have existed since the 80s). But on the other hand, the large-scale hiring of people to do this work is pretty new, as well as the volume of data available to even moderately sized services, so there's a quantitative change that is somewhat qualitative. I'm also interested in the fact that there is no mention of A-B tests in the OP, given that the need for this is relatively new, and something that probably pushed a lot of companies towards needing more quantitative people, which led to the current nature of "modern" data science.

minimaxir 7y ago

As a current data scientist, the problem is that the concepts behind data science/big data are legitimate solutions to business problems, but the current marketing of the field is either misleading (e.g. it's not all about building models/AI, and I wrote a post complaining about that: https://minimaxir.com/2018/10/data-science-protips/ https://minimaxir.com/2018/10/data-science-protips/ ), or it's business upsell for an enterprise product that may not be the optimal solution to a given problem. It's one of the reasons I've switched more to publishing open-source tooling for data science/AI instead of blog posts about data science.

bunderbunder 7y ago
The obsession with "AI" is particularly fascinating. 10 years ago, before it was a household term, people could mention without blushing that their production implementation ended up being a simple if-statement. If anything, being able to come up with an insight that was clear and well-focused enough to be implemented in such a straightforward way was a feather in one's cap. Nowadays, if you aren't building a model with at least a few million parameters (though billions would be better), that ends up being so complex that the only hope for productionizing it is to stick it into a container and ask the engineering and ops folks not to peek inside it too carefully - sort of like a parent leaving the door to their teenager's bedroom closed - then nobody wants to take it seriously. Call me a curmudgeon, but it doesn't half remind me of a while back when I was being directed to process 12MB datasets on a distributed computing cluster. At some point the tools stopped being a means to an end, and started being the only thing anybody cares about.
- deleted 7y ago
  [deleted]
- salty_biscuits 7y ago
  It is funny, what you are usually supposed to be replacing /automating with ML is the nasty 100 line conditional statement that forms some bit of "business logic" that nobody really understands. This thing usually being the time pressured fever dream of some dev that left the company years ago. Now it is replaced by something that is even more incomprehensible. If it is done well then at least you get performance guarantees and the ability to retrain. If it is done badly, then you don't know what the features are, which model belongs with which dataset, how to regenerate training sets, where the original training set went, how to set the hyperparameters to get reasonable training performance, etc, etc.

usgroup 7y ago

I’ve been reading about “expert systems” of late for a particular gig I’m pitching for ... most the literature is 1980-1985 and what I realised was is that expert systems were all the rage back then. The IBMs of this world sold them and you clearly didn’t know what’s best for you if you weren’t buying them. Is there anyone sufficiently old to remember AI in the 80s reading this? What was the job title du jour back then ?

mr_toad 7y ago

When the term was first coined it had a clear meaning - someone who was a statistician, and a programmer, and a subject matter expert. Someone who could, for instance, write code from scratch to calculate maximum likelihood estimates, and use it to make predictions on real world data. The term got coopted by snake oil salesmen pretty soon after.

bigred100 7y ago
Wait now he has to write his own optimization routines too?