6 ms·

I might be under-informed but "data science" seems to involve a lot of vague BS. Computing has always centered on data. "Data practitioners", "data practice",

• ramraj07 7y ago
  A good data scientist is a jack of all trades - mediocre programmer, mediocre ML modeler, mediocre ETL architect, mediocre statistician and a mediocre analyst. You hire this person because you don't need a specialist in each of these fields But you need someone who can do these things. Also you don't want to pay a metric ton of money so you can't expect some superstar.
  • EForEndeavour 7y ago
    > Also you don't want to pay a metric ton of money Doesn't the job title of "data scientist" still correlate with high pay, or has the hype started to wane?
    • ptd 7y ago
      Depends on the location and company. In a smaller city working for a smaller company an entry level data scientist might be happy to pull in 60k. In a major city working for a large company you can at the very least double that number.
    • TomMarius 7y ago
      If the person can do all of these in a way that makes them a profitable investment, it doesn't really seem so high
    • ramraj07 7y ago
      A data scientist doesn't command the same premium as a machine learning engineer or a kubernetes expert, I'm afraid
    • natalyarostova 7y ago
      Depends where. I'm at Amazon, and they are really serious about not letting DS become just business analysts who just SQL/Python. But elsewhere I do get the impression there is some dilution. Although honestly, that's natural. Just like how in the SDE world there are template web devs and hardcore infrastructure guys, who get paid massively different amounts but have the same job title, that's happening to DS.
  • sin7 7y ago
    Good data scientists also need to be great at a couple of things. They must be excellent presenters, and excellent diplomats. Getting the data is the hardest part. Data silos are create all over the place and getting access to that data requires speaking to a lot of higher ups.
  • WhompingWindows 7y ago
    These people often have PhD's, I doubt they are mediocre statisticians or mediocre analysts.
    • tryitnow 7y ago
      Hmmmm. I wish your implication was correct, but I fear it's not.
  • natalyarostova 7y ago
    Can confirm. Am data scientist. Am mediocre at everything :)
• CharlesW 7y ago
  The term was coined nearly 20 years old by some accounts, but is probably older. This is a good summary of its meaning and history: https://en.wikibooks.org/wiki/Data_Science:_An_Introduction/A_History_of_Data_Science https://en.wikibooks.org/wiki/Data_Science:_An_Introduction/...
• larrydag 7y ago
  Data Science as a practice is not anything new. There has always been practitioners of statistics, math, prediction, and so on. What is new is the merging of technologies that harnesses the large amounts/kinds/disparities of data that is being stored and processing it into useful information to drive decisions for an organization. Thus a term was coined "Data Science".
  • deleted 7y ago
    [deleted]
• minimaxir 7y ago
  Data science, unfortunately, is often considered a buzzword because it varies so significantly from org to org, and often there's a lot of No True Scotsman arguments resulting from that which makes everyone unhappy. But at minimum, every data scientist needs to know SQL; there's no way around it. I wrote a full blog post ranting about the disparity between expectation vs. reality for data scientists a year ago: https://minimaxir.com/2018/10/data-science-protips/ https://minimaxir.com/2018/10/data-science-protips/
  • throwawayjava 7y ago
    > But at minimum, every data scientist needs to know SQL; there's no way around it. Is it no longer the case that every programmer has to know SQL? I have always and still do consider knowledge of SQL a minimum requisite for any programming job...
    • jkaptur 7y ago
      It is not the case that every programmer has to know SQL (if it ever was). Programming is now such a big field that plenty of people will go their whole careers without knowledge that's foundational for other programmers - whether it be SQL, linear algebra, memory management, distributed systems, ... In a way, it's very cool. Instead of a single-dimensional "how good a programmer are you", we've got a multidimensional space. That, naturally, yields a lot more interesting corners, like, say, what could be invented by someone who knows a lot about databases and GPUs.
    • minimaxir 7y ago
      Granted, SQL is an extremely useful skill for programmers, but there are use cases (e.g. front-end dev) where it's not required.
    • zild3d 7y ago
      There are plenty of programming jobs that have no need for SQL, it's just easy to forget how broad the field is. There was never a need for SQL when I was programming microcontrollers, or DSP work in python/matlab
• hobofan 7y ago
  Just because "computing has always centered on data", doesn't mean that every aspect of that was done well (not implying any recent changes brought by "Data Science" here). For a lot of systems, a lot of engineers/programmers often care very little about the "data", and they very often don't need to. E.g. on a high abstraction level your webserver doesn't care if it's sending HTML, JSON or whatever, and here we are just speaking about formats and not even higher semantics of the data. I think there can be a lot of value in data-focused roles in companies, because there are many (hard) questions around data that are often badly answered as afterthoughts: - What are the exact semantics of this data? - How do make sure the data is clean? - How do we evolve the data over time?
• rm999 7y ago
  >Computing has always centered on data Sure, well-thought out data structures were an integral part of good clean code, but the bulk of data was an artifact of software in the form of logs and largely unused database records, not the "center" of anything. Today, there are many people who take these logs and database records and run very specialized code on them. I entered industry in the mid 2000s and it was very difficult to find jobs that concentrated on this component back then. We're often called data scientists today, and there's orders of magnitude more attention that goes into it. This is a real shift that has brought data into the "center" of many organizations.
• 131012 7y ago
  I'd say that data science adds some domain knowledge to computing. Usually, the domain knowledge is in business analytics, but it could also be medicine or social sciences. Where traditionally trained computer scientists fail is not so much at implementing the solution, but being able to gain beneficial insights either from the needs of the clients or from the results of computation. After a new round of hiring, we came to the conclusion that "pure" computer scientists will not do the cut, even those with a phd, simply because we are quite convinced that they won't be able to understand the problem at hand.
• mumblemumble 7y ago
  Perhaps a more useful characterization would be that it's what happens when you take work that would traditionally belong to statisticians and actuaries with only limited programming and IT skills, and give it to someone who's a more of a jack-of-all-trades programmer/statistician. I don't think that this leads to changes in the traditional ways of handling IT because of any fundamental change in a data analyst's needs. It's more that, historically they were outside the formal tech sphere, and therefore lusers who had to make do with whatever resources they were able to squeeze out of IT. Now, armed with better technical chops, they're able to make a stronger case for their needs, which means that more is being done to meet those needs. Which is not to say that your skepticism is unwarranted. "Data science" is still a catch-all term for a new and ill-defined way of working, and things are still pretty wild west. Anyone who tells you they're sure they know what the best practices are is probably trying to sell you their product, which happens to be the best practice. Not many people have the benefit of 20 years of hard-won experience from which to draw a set of really strong guidelines.
  • alexcnwy 7y ago
    The big difference is that historically you could run pretty much all analysis on a single computer (if not in Excel - I’ve seen and built many actuarial models in Excel myself) but consumer internet scale data (both in terms of the number of rows and the number of features available) is too big to model without distributed computing and more programming work on the part of the analyst. Moreover it’s an iterative process where you start with hypotheses on what features will be useful then build a model and might need to pull more data. For example you could build a recommendation engine just using the set of products purchased by customers but then might explore adding user agent features like devices or might add data about the products like category or price information. An analyst (“data scientist”) who can pull and interact with that data is much more effective than one who needs to wait for someone else to pull the data for them (most actuaries I know are weak programmers outside of R and wouldn’t know where to begin using distributed computing or getting a TensorFlow gridsearch running on a cluster of cloud GPUs :)
    • mumblemumble 7y ago
• mcnnowak 7y ago
  Sounds pretty dismissive. AI and Big Data are buzzwords that shouldn't be used very often. Data Science is about creating solutions that are right for the size of your data, and the questions being asked of it. A C++ program with a couple megs on the heap is appropriate, just as an ETL pipeline is appropriate for gigabytes to terabytes.
• davidw 7y ago
  The joke is that a data scientist is a statistician who works in San Francisco.
• Macha 7y ago
  Isn't this sort of marketing of a fancier job title not a theme in the entire industry? Programmer -> Developer -> Software Engineer Sysadmin -> Operations Engineer -> Site Reliability Engineer QA Engineer -> Automation Engineer -> SDET
• prepend 7y ago
  I used to work for a big consulting firm. A pretty fancy one, not the fanciest, but fancier than most that claim to be fancy. 10 years ago they had a big initiative around “Analytics.” I’ve programmed and worked in data for a few decades so I tried to figure out why these biz people were interested in Analytics. When I asked they brought up all the terms you said. I showed examples from various projects how they were just describing basic data programming. But they disagreed because this was new. I figured it out a few years later when they spent $5B on the initiative and it was making money like gangbusters. This was now a product line. It was new that they were selling it. So now there’s people who want more money and need to differentiate and thus the titles and whatnot. I wish there was an easier way to view work output of people because now the only way I can differentiate between “data engineers” and “data scientists” and “AI wizards” is by looking at their GitHub profiles or talking to them in depth and profiles are almost never complete or accurate and talking takes a lot of time.
• opportune 7y ago
  I think the term “data science” has largely been co-opted by data analysts and SQL analysts. At a big software company that you’ve definitely heard of, “data scientist” consultants were people who created daily reports for upper management that they created by clicking through graphical UIs charting usage data (ie we gained a lot of users in this region, because of this customer, or lost some because of a holiday). Data scientist employees were SQL analysts and data engineers. In my experience most of the people doing what I consider data science have the titles “applied scientist” “machine learning engineer” “data engineer”.