>> Live from Stanford University. It's the Cube covering Global Women and Data Science Conference brought to you by Silicon Angle media. >> Welcome back to the Cubes Coverage of the fourth annual Women in Data Science Conference. This global winds event is the fourth annual our fourth year here, covering it for the Cuban Lisa Martin, joined by Gianluca Pecorino, the director on the Stanford Institute for Computational and Mathematical Engineering. Gianluca, it's a pleasure to have you on the program. Thank you. So the Institute for Computational and Mathematical Engineering. I see M e. Tell us a little bit about that and its involvement in wins. >> Yes, so the status has. Bean was funded fifteen years ago at Stanford as a really hard before computation of mathematics at Stanford. The intention was to connect computations and in general, the disciplines around campus towards using computing for evolution, for starting new ideas for pursuing new endeavors. And I think it's being extremely successful over the years in creating a number of different opportunities. Now weeds started four years ago. As you mentioned, it's part of an idea that the prior director advising me, Margo Garretson, had with few others, and I think the position of I see me at the center of campus really helped bring these events sort of across different fields and this different disciplines. And I think, has Bean extremely successful in expanding and creating a new, a completely new movement, a completely new way of off off engaging with with a large, very large community. And I think I seem, has Bean very happy to play this role? And I'm continuing to be excited about the opportunities >> you mentioned expansion and movement to things that jump out. Expansion way mentioned fourth annual on Lee started This Is three and a half years ago knew that twenty fifteen and we were had the pleasure of having Margo Garrett send one of the co founders of Woods on the Cube last year at wigs. And I loved how she actually said. Very cheeky winds really started sort of as a revenge conference for her and the co founders, looking at all of the technology, events and industry events and single a lack of diversity. But in terms of expansion, this there are one hundred fifty plus regional winds events this year in fifty plus countries. They're expecting over one hundred thousand people to engage this expansion. In this movement that you mentioned, it's palpable. Tell us about your Where's the impetus for you to be involved in the woods movement. >> Well, I think my interest in in data science and which particular is because of the role that I seem years in the education at Stanford. We obviously have a very important opportunity toe renew and remodel our curriculum and provide new opportunities for for education off the new generations and clearly starting with with the opportunity off being such an audience and reaching so many different discipline. It's a very different fields. Helps us understand exactly how to put that curriculum together. And so my focus of my interest has been mostly on making sure that I see me alliance with these new directions. And when we establish the institute, computational mathematics didn't really not have data is a very, very critical component, but we are adjusting to that clearly is becoming more and more important. We want to make sure we are ready for it, and we make sure that the students through our curriculum are ready for the world out there. >> So let's talk about this. The students and the curriculum. You've been a professor at Stanford for a very long time before we get into the specifics of today's curriculum. Tell me a little bit about how you have seen that evolve over time as we know that. You know, we're sort of in terms of where the involvement and women and technology and stump field words in the eighties and how that's dropped off. Tell me a little bit about the evolution in that curriculum that you've seen and where the ice Amy is today with that adaptation. >> Yes, certainly. The evolution has bean very quick. In the last few years, we have seen, um in a number of opportunity emerging because of the technology that is out there. The fact that certainly for data science, both the software and the artwork and the technology, the methodology, the algorithms are all in the open so that there is no real barrier into sort of getting started. And I think that helps everybody sort of getting excited about the idea and the opportunity very, very quickly. So we don't really need to goto an extensive curriculum to be ableto ready, solve problems and have an impact. And I think that, perhaps is one one other reason why we are sort of in a level playing field right. Everything is is available to everybody with relatively minor investment at the beginning. And so I think that certainly a difference with respect what the disciplines, where instead, it was much more laborious process to go through before you can actually start having an impact. Suffering every o opportunity, toe change world to toe come, you know, sort of your your vision's sort of impact in the world. So I think that's That's definitely something that the data science and the recent development into the science have created. And so I think, in terms of our role, sort of continuing role in this is tow Pet Shop six. You know, expand the view ofthe data. Science is not just the algorithm, the technology, the statistical learning that you need to accomplish. A student is a new comet into the field, but also is other other elements. And I would say certainly the challenges that we are that are opposed to data. Since they are challenges that have to do with the attics with privacy on DSO, these are clear, clearly difficult to handle because they require knowledge across disciplines the typical air not related to stem in In a traditional sense. But then, on the other hand, I think is the opportunity to be really creative. Data is not analyzing on its own right. He needs the input are sort of help in creating a story. And I think that's that's another element that he makes data science a little bit different. Another stem disciplines intend to be much more ascetic, much more sort of a cold if you like. I think >> that's where the things to you that I find really interesting is if you look at all the statistical and computational skills as you mentioned, that a good data scientist needs to have as we look at some of the challenges with the amount of data being created. So you mentioned privacy, ethics, cybersecurity issues. The create creative element is key for the analysis. Other things, too. That interest me, and I'd love to get your thoughts on how you see this being developed on the curriculum. Helping is is empathy, collaboration, communication skills. Where is that in the curriculum and how important you are? Those other skills to the hard skills >> that that's That's a great question. And I think where is in the curriculum? I think we're lagging behind that. This is one of the opportunities that we have to actually connect to our other places on campus, where instead the education is built much more closely around some of these topics is that you mentioned. So I think you know, again, I the real advantage in the real opportunity we have is that the data science in general reaches out to all these different disciplines in a very, very new way if you like. I think it's it's probably one of the reasons why so attractive toe younger generation is the fact that it's not just the art skills. You do need to have a lot off understanding of the technology, the foundational statistics and mathematics and so on. But it's much more than that. Communication is very important. Teamwork is extremely important. Transparency is very important. There are there are really all these elements that do not really make that they really didn't have a place in some of the more traditional dissidents. And I think that that's definitely a great way off. Sort of refreshing are way off, even considering education and curriculum. >> When you talk to some like the next to the younger generations. Is that one of the things that they find are they pleasantly surprised, knowing that I need to actually be pretty well rounded to me? A successful data scientists? It's how I analyzed the data. How I tell a story, is that something that you still find that excites but surprises this younger generation of well, that >> certainly is a component, very important component of the excitement of the sea. Are there the fact that you can really build the story, tell a story, communicated story and oven, in fact, immediately, quickly, I think is a is something that the newer generation really see it assess a great opportunity and, you know, and it tried to me. So I mean, it has been very difficult for more traditional disciplines to have the same level of impact, partly because the communities tend to be very close, very limited with with a lot of scrutiny. I think what we have in India, the scientists, that is really a lot off you no can do attitude the lot off, Really. You know, creative force that is >> behind, you know, >> basically this movement, but in general data science, I think that >> you write. The impacts is so potent and we've seen it and we're seeing it in every industry across the globe. But it's such an exciting time with Gianluca. We thank you so much for sharing some of your time on the program this morning and look forward to hearing more great things that the ice Amy is helping with prospective women in Stem over the next year. >> Absolutely. Thank you very much. >> My pleasure. We want to thank you. You're watching the Cube live from the fourth annual Women and Data Science Conference here at Stanford University. I'm Lisa Martin. Stick around. My next guest will join me in just a moment.