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I think what companies really want is smart generalists with advanced math, programming, and modeling skills coupled with domain knowledge. That skill set will

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 in data science. Rather than pursuing problem solving, people are pursuing "data science" which is a nebulous term in and of itself.

Ntrails 7y ago
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.
- seisvelas 7y ago
  Yep, that's why I make sure to set time aside for toy problems. Creating contrived problems can sometimes scratch that itch to use a certain technology that don't really fit into what I'm doing at work.
- mr_toad 7y ago
  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.
  - geezerjay 7y ago
    > 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.
    - deleted 7y ago
      [deleted]
    - developorrRRRGH 7y ago
      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.
  - otabdeveloper1 7y ago
    > 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.
  - mattkrause 7y ago
    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.
usgroup 7y ago
> The recent decline in interest... Can you quote a source for this?
- lhotkins 7y ago
  Yes I'd be quite keen to see that also, I wasn't aware there was a decline already (although it's obvious the interest can't just keep going up forever).
roystonvassey 7y ago
Yes. Unlike software development, data science is not completely business agnostic and a fair amount of business understanding is required. For e.g. if you work with sales data, you need to be aware of seasonalities, purchasing patterns etc to understand the trends that you observe to discern between what is a true outlier and what is explainable. What makes me cringe the most is to see flashy presentations with claims akin to 'Data Science will change your world'.For sure, it can and has been proven to automate decisions (think, credit scores), assist in decision-making (think, sales trends) and anomaly detection (think, security systems). I find so many data scientists that I interview are so hung up about the esoteric techniques they employ, often failing to even explain why was it useful or how it helped their businesses. What has been transformational and path-breaking is the breaking of enterprise monopolies in this space (for e.g. SAS/IBM SPSS) and a variety of open-source frameworks have made it easy and convenient, apart from opening it up to software developers to build these skills. Important, though, to not lose of the sight that data science is at the sweet spot of expertise in domain, data and technology.
Rainymood 7y ago
>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.

Ntrails 7y ago

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.

seisvelas 7y ago
Yep, that's why I make sure to set time aside for toy problems. Creating contrived problems can sometimes scratch that itch to use a certain technology that don't really fit into what I'm doing at work.
mr_toad 7y ago
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.
- geezerjay 7y ago
  > 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.
  - deleted 7y ago
    [deleted]
  - developorrRRRGH 7y ago
    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.
- otabdeveloper1 7y ago
  > 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.
- mattkrause 7y ago
  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.

usgroup 7y ago

> The recent decline in interest... Can you quote a source for this?

lhotkins 7y ago
Yes I'd be quite keen to see that also, I wasn't aware there was a decline already (although it's obvious the interest can't just keep going up forever).

roystonvassey 7y ago

Yes. Unlike software development, data science is not completely business agnostic and a fair amount of business understanding is required. For e.g. if you work with sales data, you need to be aware of seasonalities, purchasing patterns etc to understand the trends that you observe to discern between what is a true outlier and what is explainable. What makes me cringe the most is to see flashy presentations with claims akin to 'Data Science will change your world'.For sure, it can and has been proven to automate decisions (think, credit scores), assist in decision-making (think, sales trends) and anomaly detection (think, security systems). I find so many data scientists that I interview are so hung up about the esoteric techniques they employ, often failing to even explain why was it useful or how it helped their businesses. What has been transformational and path-breaking is the breaking of enterprise monopolies in this space (for e.g. SAS/IBM SPSS) and a variety of open-source frameworks have made it easy and convenient, apart from opening it up to software developers to build these skills. Important, though, to not lose of the sight that data science is at the sweet spot of expertise in domain, data and technology.

Rainymood 7y ago

>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.