<p>^ Yeah, that’s the thing, anyone with a math degree who learns statistics + some good amount of programming is set to at least be trainable for a pretty good job.</p>
<p>That’s true from what I hear too. Even engineers and computer science majors at my school, which is very solid for those majors, tend to think someone aiming to do serious studies in math is very exceptional or something, having struggled with the more basic material themselves at some point. This holds true even more of a PhD. And these remarks probably carry over to some extent to a physics theory person, because I know plenty of engineers are terrified by the higher level physics courses, if indeed they have to take them.</p>
<p>The thing is, a math program in the standard sense teaches you “how to think” while not teaching real world data analysis, programming, etc, which are important to make day to day decisions, and actually implement ideas into software that can then be used. This transition is very doable for a motivated individual, but not one every theoretically oriented person is willing to make.</p>
<p>This is not a good excuse not to major in math, because indeed, math majors tend not to require very many courses, and in order to develop the reasoning facility (which is certainly something most of the quantitative world lacks) and familiarity with how to actively think about the subject (rather than learn the names of a few results and throw them out), this seems to be the minimum. But to <em>actually be able to apply math</em>, I think you have to be pretty serious. And I’m talking math the way it’s taught in school, not a somewhat related degree such as the Statistics degree. It’s almost certain that graduate training is necessary, because it takes a very long time to get a feel both for the subject math and how to actually use it. By nature, the training begins with fundamentals, because it kind of has to - the subject is fundamentally not an applied discipline.</p>
<p>I make this long post, because the transition from basic math => university level significant math reasoning => using THAT level of reasoning in applications is actually a very, very significant leap. I think to put it this way, you need to spend several years training in math and sampling applications of it, and then do graduate training (though certainly not necessarily a PhD) in some area where you want to actually apply the subject specifically, and deeply study how to do so.</p>
<p>Now there’s another story which says that as a math major, you can forget about math and learn other skills like stats and programming, and get a good job <em>because you are perceived as smart and have applicable skills</em>. This is also true.</p>