AlexS-B

The MS Access database of every Floridian prisoner's tattoos was pretty interesting (I think we stumbled over it on Reddit, actually), and the 1.5TB of dark web scrapings yielded a nice story too.

Hello greencracker, we do a lot of our data exploration and analysis in R and Python (and even Excel), then finish off our charts in Illustrator. For interactives, we use D3.

You definitely don't need to major in math or do graduate work--as long as you have the skills, no one will care how you acquired them. There are lots of programs like Coursera and General Assembly where you can learn outside of an academic setting. Within a university, coursework in statistics, econometrics, and/or computer science is probably more relevant than pure math. You definitely need to know how to code, typically in R and/or Python. In terms of modeling, you'll learn a lot about regressions from a typical economics path. Machine learning might be more in the computer-science realm.

Pretty good at the moment, but getting better all the time. I'd give it a solid 8/10.

We actually have an article in the works on the weaknesses of a lot of the survey data that many macroeconomic statistics are based on. Our own projects usually don't require such large datasets (though there are exceptions, like the Lebanese census piece). But even when they do, it's important to keep the alternative in mind: if your data aren't perfect but you don't expect the flaws to reflect systematic biases, they're still a valuable addition to non-quantitative information like anecdotes and expert opinion.