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On the flip side you used to have statisticians writing code that is frankly unusable in a Production environment. You would weep at the R code I've seen and ha
by adamsmith143 4y ago
On the flip side you used to have statisticians writing code that is frankly unusable in a Production environment. You would weep at the R code I've seen and had to turn into something to actually produce business value.
- esparrohack 4y agoYeah but in the end it’s just code. And even better, just R. The business value comes from the stats guy.
- adamsmith143 4y agoWhen the R/stats guy quits and you have to figure out which of his 7 notebooks to run in which order and which local files need to be in which local directories to run correctly and which versions of each package are now broken and which code you need to rewrite to fix it you start to realize the value he produced was clicking a lot of buttons in the right order and that overall this doesn't scale at all.
- mellavora 4y agoThat sounds more like a jupyter notebook/python problem than an R problem. but otherwise, yes, I see the problem.
- adamsmith143 4y agoThe hours I have spent debugging package problems in R would disagree.
- esparrohack 4y agoI know that pain. That’s why I’m saying avoid it if you can do so.
- esparrohack 4y agoYeah, but I meant that because the business value is in the stats, and there is such low quality of stats in the field to begin with, it’s borked no matter what. There’s no point in fixing it. You can just pretend like you did. But if the stat work is quality, then it’s worth the effort to optimize.
- drgiggles 4y agoThis is exactly my point. Let subject matter experts in their respective disciplines handle what they know and communicate through the lingua franca of R. Most data scientists/statisticians probably shouldn't be writing production code, I think that's ok. It's a failing of management to think that coding is coding and not understand the value of true engineering ability.
- numbsafari 4y agoMy first job basically consisted of taking code in FORTRAN and translating it into C++ with robust testing and engineering, and then frontending that code into a ton of spreadsheet packages. So you had quanta doing quant work, software engineers doing software engineering, and analysts and traders being analysts and traders, instead of having quants fail at all three, which is more or less what data science is.
- fnands 4y agoThere is a bit of a joke that a data scientist is someone who can do better stats then the average SWE and can write better code than the average statistician. Both of those are relatively low bars to clear though
- ketzo 4y agoThe way I heard the joke was "a data scientist is someone who's not good enough at math to be a statistician, and not good enough at programming to be a software engineer." Maybe a little harsh...
- disgruntledphd2 4y agoThat's much better. Consider that stolen.
- fnands 4y agoHarsh, but funnier than how I phrased it.