One Year as a Data Scientist at Stack Overflow

Hey, I'm the Jason Punyon drob's gushing about in the post. I wanted to tell you that this is 100% totally realistic, everyone on the Data team at Stack Overflow besides Dave Robinson is living proof.

In the beginning the Data Science Team at Stack Overflow was just me and Kevin Montrose. We're both ~30, we had some skills that were data-ish, but they were general critical thinking and math skills. By no means were we statisticians or data scientists by trade like Dave Robinson. However, the data at Stack Overflow was very amenable to analysis, and we were able to ship a personalized machine learning product after a year of work.

That year was plagued by (my, mostly) engineering failures (http://jasonpunyon.com/blog/2015/02/12/providence-failure-is...) and a bit of unfortunate lack-of-empiricism, not "data science" problems. And post launch of Providence, the engineering remains the bigger issue. It's just hard to get this shit right. Our record on getting experiments right the first time is spotty, and at Stack Overflow levels of traffic you might be dumping a week or more of time on a single screwup. We've had to rerun many experiments because of various things going wrong with the code (screwed up the A/B testing code, not counting the right things, visual issues with the ads we didn't catch during design, etc etc etc). There's a litany of things you've gotta get right engineering wise before data even comes into the picture.

So if you're gonna take the plunge, know that it's 100% possible but 100% difficult, and maybe not for the reasons you think.

I hire plenty of data scientists - if I had to give a single piece of advice: gain a deep understanding of the techniques you're using.

it's not sufficient to say "oh, we used a support vector machine", and when prompted for more detail about how it works to shrug and say you just copied and pasted code from the internet and tweaked parameters until it worked.

I'm also a self taught programmer. I recently started using https://www.dataquest.io/ (not affiliated with them) and have found it to be a great data science resource for people who prefer a hands-on approach to learning. Previously I'd tried various MOOCs but the lecture format with only a few questions to work on was not helpful for me.

Remember all this data comes from Stack Overflow. Some of the clusters might look a bit odd, but that's because they're all derived from the relationships we've found in Stack Overflow content.

Excel is a great tool, but its graphing ability leaves a lot to be desired. I feel that graphing in Excel has gotten worse as they have tried making it easier to use as well. I'm 99% sure the graphing engine in Office 2007-2016 is the same one as in 2003 and earlier, but all of the damn menus they have added slow down the graph making process so much. If you are trying to use Excel to make a graph for a reasonably technical audience you are going to need to make a lot of tweaks to the graph to make it acceptable, and tweaking each part of the graph takes dozens of clicks where it used to take one or two.

I personally think that Excel is one of the best tools I have ever used. I do about 40% of my work in Excel. I graduated from college with a degree in biology, and I spent hundreds or thousands of hours in Excel manipulating data and graphing. Excel can and will graph almost anything you need to graph, but anything more complex than the simplest line graph requires getting creative with the formatting of your data and how you use series and data sets. The maximum complexity of a graph in Excel is technically restricted by the 254 series limit, but creating a graph with 254 series will probably take the better part of a week.

So ultimately, Excel isn't a bad tool, but when graphing it's a bit like using a shovel to dig a hole. It has a time and a place, but at a certain point using an excavator will be faster, safer and less expensive than digging the hole by hand.