(upbeat music) - Data's been around forever. You know, who are the original data scientists? Well, the Mayans did a heck of a lot with data to figure out astrology, to figure out how to plant crops more efficiently. Ancient Indians did a great job, the Chinese have done a phenomenal job of using data in creative ways. We go to Copernicus and Kepler, who used data to figure out planetary orbits.
You know, you could call these people data scientists, somebody will say, well, what's the difference between a data scientist and a scientist? The genesis here now is we have the ability to collect data in powerful ways. We also have that ability to turn it into products and develop new things. What's interesting is if you're a scientist, a mathematician, a physicist, and you wanted to go into a business and say hey, I can add value, they'd say, sorry, you're a scientist. But somehow when you add the term data in front of it, now you can add value.
So part of this is instead of getting hung up on the term data versus scientist or data scientist and what are all these things, I wish we were in a world where we asked what skills could be provided to a problem. You have skills, this person has skills. What's the right type of team, if it was to come together, could unlock the potential of a data set or a set of problems through that disparate set of, you know, skills, technologies, and ideas? - [Interviewer] You're worried about the skill set, not necessarily the title.
- Right, well, the first thing I think that's really important is you don't need a PhD to do data science. You know, it's unbelievable how many times I see organizations, they're like, we need a PhD to do this. You don't. There are so many creative people that I have met who have no formal educational training, and just are brilliant. They've just figured it out on their own. The same way also people who go through formal training also have an incredible amount to offer, but often don't have a pathway into an organization.
And part of it is because they are viewed only as a scientist, and we actually started a program, it's called the Inside Data Science Fellowship Program, and it literally was, in a model of, for people like me, who tried to come into an industry and couldn't because we didn't know the vernacular. We didn't kow the style, or that. So we take six weeks, we take these PhD and academics, and we kind of train them in the sense of here's how you talk to a company. Here's how you work on a team, here's how you check in code, all these different things.
And they get picked up like that. And they are so incredibly successful. People go, you do an amazing job of training them to be a data scientist in six weeks, and we're like, no. No we didn't. We just helped create the interface pipe between these two worlds. We created the Venn diagram overlap. And then once they're in, they're able to do the work and add value. What would that look like if we opened that up more broadly and we didn't say that's just true for data scientists, but that someone out of another liberal arts background or some other type of background, like, we didn't just say, well, you don't have an MBA.
What if we just said, you know what, you're just good. You're good, or we allowed you to partner with somebody who you're able to apprentice into something. That, I think, is a more powerful way to create not just good culture inside an organization, but to leverage the broader set of things that people can offer and make a richer, a problem more rich, a company more rich, because you're making the comprehensive skill sets of the universe bigger.