> It also required enough math that I could've gotten a dual degree in math by taking (IIRC) two more courses That's my point. I did a double major in Math/Eco

> It also required enough math that I could've gotten a dual degree in math by taking (IIRC) two more courses That's my point. I did a double major in Math/Econ with a minor in Applied Statistics. I could have switched 2 courses away from my Econ major and instead received a minor in CS. There are countless boot-campers that just didn't take enough 300 level courses to put it on their resume. Looking back on my degree and my career so far, I can't say I've used the phrase "generalized autoregressive conditional heteroskedasticity" in a context other than a joke or in conversations exactly like this. That's from Stats-3xx time series analysis. I worked as an equities trader for the first half of my career before switching to programming. It's harder to get more time-series analytical than that. Who uses that phrase? The PhD econo-quants and risk managers who spent a bit more time studying it than the 2 weeks I went over it in an alcohol filled second semester of my senior year. I'm simply saying, undergraduate degrees aren't much more than a rubber stamp. Sure, you may have learned more about data structures than I did. We both probably took some form or another of graph theory, linear algebra, algorithm design, etc. What you learned in a semester of data structures, I'm sure I learned 95% of it in a year on the job. Similarly, if you decided to go into finance, I'm sure you could pick up all the meaningful parts of macroeconomics in a year. Because that information is USEFUL in the day-to-day. If you decided to apply to a job as an equities trader, you wouldn't be dismissed because you don't know how to implement a model you will never use.* * unless, of course, you decide to work towards being a quant. In which case, you'll probably know more about it than I do in 2 weeks. * poof *, there goes the value of my undergraduate degree.