(upbeat music) - What's been shifting radically, even in the space of training, is the amount of people who go into economics now, as in their undergraduate degrees, who are taking data science classes in machine learning. Because why, because they're now starting to work on messier data sets. People who are doing quantitative behavioral sciences or other types of things are also taking those same classes. We're kind of merging all of these things into the Venn diagram of overlap. Problems get solved by having people who kind of go across a spectrum and bring all sorts of wide resources to bare to figure it out. To me, the greatest mistake that people make, they tried to find their box rather then trying to make their box amorphous. How do you have deep, deep specialization, but also broad? It's what Daniel Pink refers to as a T. You have to know something incredibly well, but you got to know a lot of other stuff. And the thing that is more critical as we're entering these rapid, shifting model of how careers work, where it's like you change careers or you kind of change this complete paradigm of environments that you work in. The number one thing is you have to have this what you could call dynamic range and in photography it's the ability to, you know, the film to be able to work across all sorts of light conditions, all sorts of different environments. You need that in your career, too. If your firmware, like you were like back in the eighties, that's not firmware today. But the people who have the greatest dynamic range can go from one problem to another and they have the wisdom, they don't just have experience, they have wisdom that gets to be leveraged in every single spot. That is what people pay a premium for. (upbeat music)
Skill Level Intermediate
Wrapping up1m 5s
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