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Data science is one of those words that are like a semantic mine, which HN seems to be especially susceptible to. These words (a few years ago it was "cloud" o

by netcan 13y ago

Data science is one of those words that are like a semantic mine, which HN seems to be especially susceptible to. These words (a few years ago it was "cloud" or "Web 2.0") catch on because they capture a bunch of things that have chained which seem related. But, they aren't clearly definable. This traps people (I think HN people especially) Web 2.0 was a trend in prominent personality types, types of websites, business models, increased scale of online interaction, use of real names, web design styles, programming languages. It wouldn't have been absurd to see a guy with a specific look and a specific laptop and say "that's so web 2.0". To add insult to injury, pretty much all the stuff that gets captured in a word like web 2.0 has predecessors. Crowd sourcing? What about Wikipedia? I think a good analogy is "movement" in art, philosophy & culture. Modernist is a word that encompasses Frank Lloyd Write, Pablo Picasso, James Joyce, Ayne Rand & karl Marx. It applies to paintings, manifestos, econometrics and buildings with straight lines. That's the kind of word that 'data science' is. We found ourselves recording a lot of data as a sort of side effect of digitization. It's growing. Then we start to try and get some value from that data. Some new stuff is possible with that volume of data. Some new people are now interested in data. A lot of the tools people were using to collect and analyze data don't work at that volume, so we start using new tools. We end up with a word that includes astronomers, netflix, medical researchers, self driving cars, R, statistical theories etc. Data science doesn't mean anything that specific yet. It's best not to lead the discussion (as I am doing right now) to a discussion about the word, what qualifies as data science.

kyzyl 13y ago
Yes, thank you. I like to distill things a bit: 1. There is science, and there is data. Science has data, and is a thing you can do. Data is just data. "Data Science" as a description of an activity is like saying "Words Writing" or "Money Banking". As a whole the term holds no concrete meaning, thus it is contorted to whatever the current topic of conversation is (this is exactly what you've just said, I think). 2. There has been a massive change in the volume of data and the tools we use to analyze it. This is nothing to do with what you're doing, what field you're in, or whether you know what you're doing. Again, data is just data. (Also what you said). 3. If you want to use the term in your conversations, blog posts or--if you must--publications, fine. Just make sure YOU have already set in stone what it is you're talking about. It shouldn't take others probing your sentence structure, nomenclature and argumentation for you to come up with a precise definition of what you mean. That's just bad SCIENCE, no matter how much DATA you have.
- netcan 13y ago
  It's bad science, but I don't think it's bad conversationally. If I say that I think the hardest part of a good streaming media service is getting the data science right, we both kind of know what I mean. It might be useful that I might mean the choice of database or I might mean algorithm. Even though the things encompassed by the term aren't necessarily related, they usually are. Google was obsessed with big data before it was cool and they were/are the best on most fronts: collecting the most data, getting between the user and the data (query->result) fastest, and getting the most correct answers. The "cloud" buzzword really pissed people off. I thought that was an overreaction too. If you say that enterprise software companies need to deal with these new cloud competitors, you mean a whole cluster of things. Web software, SaaS pricing models and sales cycles, smaller companies that don't run their own infrastructure, etc.

by netcan 13y ago

kyzyl 13y ago

Yes, thank you. I like to distill things a bit: 1. There is science, and there is data. Science has data, and is a thing you can do. Data is just data. "Data Science" as a description of an activity is like saying "Words Writing" or "Money Banking". As a whole the term holds no concrete meaning, thus it is contorted to whatever the current topic of conversation is (this is exactly what you've just said, I think). 2. There has been a massive change in the volume of data and the tools we use to analyze it. This is nothing to do with what you're doing, what field you're in, or whether you know what you're doing. Again, data is just data. (Also what you said). 3. If you want to use the term in your conversations, blog posts or--if you must--publications, fine. Just make sure YOU have already set in stone what it is you're talking about. It shouldn't take others probing your sentence structure, nomenclature and argumentation for you to come up with a precise definition of what you mean. That's just bad SCIENCE, no matter how much DATA you have.

netcan 13y ago
It's bad science, but I don't think it's bad conversationally. If I say that I think the hardest part of a good streaming media service is getting the data science right, we both kind of know what I mean. It might be useful that I might mean the choice of database or I might mean algorithm. Even though the things encompassed by the term aren't necessarily related, they usually are. Google was obsessed with big data before it was cool and they were/are the best on most fronts: collecting the most data, getting between the user and the data (query->result) fastest, and getting the most correct answers. The "cloud" buzzword really pissed people off. I thought that was an overreaction too. If you say that enterprise software companies need to deal with these new cloud competitors, you mean a whole cluster of things. Web software, SaaS pricing models and sales cycles, smaller companies that don't run their own infrastructure, etc.