(energetic music) >> Announcer: Live, from Stanford University in Palo Alto, California, it's The Cube, covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to The Cube. We are live at Stanford University at the third annual Women in Data Science Conference. I am Lisa Martin. We've had a really exciting day so far, talking with a lot of female leaders in different parts of STEM fields. And I'm excited to be joined by my next guest, who is a speaker at this year's WIDS 2018 event, Daniela Witten, the Associate Professor of Statistics and Biostatistics at the University of Washington. Daniela, thanks so much for stopping by The Cube. >> Oh, thanks so much for the invitation. >> So here we are at Stanford University. You spent quite a lot of time here. You've got three degrees from Stanford, so it's kind of like coming back home? >> Yeah, I've spent from 2001 to 2010 here. I started with a bachelor's degree in math and biology, and then I did a master's, and finally a PhD in statistics. >> And so now you're up at the University of Washington. Tell us about that. What is your focus there? >> Yeah, so my work is in statistical machine learning, with applications to large scale data coming out of biology. And so the idea is that in the last ten or 20 years, the field of biology has been totally transformed by new technologies that make it possible to measure a person's DNA sequence, or to see the activity in their brain. Really, all different types of measurements that would have been unthinkable just a few years ago. But unfortunately, we don't yet know really how to make sense of these data statistically. So there's a pretty big gap between the data that we're collecting, or rather, the data that biologists are collecting, and then the scientific conclusions that we can draw from these data. So my work focuses on trying to bridge this gap by developing statistical methods that we can use to make sense of this large scale data. >> That sounds exciting. So, WIDS, this is the third year, and they have grown this event remarkably quickly. So, we had Margot Garritsen on the program a little bit earlier, and she had shared 177 regional WIDS events going on today, this week, in 53 countries. And they're expecting to reach 100,000 people. So, for you, as a speaker, what is it that attracted you to participate in the WIDS movement, and share your topic, which we'll get to in a second, what was it that sort of attracted you to that? >> Well, first of all, it's an honor to be invited to participate in this event, which, as you mentioned, is getting live streamed and so many people are watching. But what's really special for me, of course, as a woman, is that there's so many conferences out there that I speak at, and the vast majority have a couple of female speakers, and it's not because there's a lack of talent. There are plenty of very qualified women who could be speaking at these conferences. But often, the conference organizers just don't think of women right away, or maybe add a couple women as an afterthought to their speaker lineups. And so it's really wonderful to be part of a conference where all of the speakers are women, and so we can really see the broad ways in which women are contributing to data science, both in and out of industry. >> And one of the things that Margot shared was, she had this idea with her co-founders only three years ago in 2015, and they got from concept to their first event in six months. >> Daniela: Women know how to get things done. >> We do, don't we? (laughs) But also what it showed, and even in 2015, and we still have this problem in 2018, is there's a massive demand for this. >> Yeah. >> The statistics, speaking of statistics, the numbers show very few women that are getting degrees in STEM subjects are actually working in their field. I just saw this morning, it's really cool, interactive infographic that someone shared with me on Twitter, thank you very much, that showed that 20 percent of females get degrees in engineering, but only 11 percent of them are working in engineering. And you think, "How have we gone backwards in the last 30 years?" But at least now we've got this movement, this phenomenon that is WIDS to start, even from an awareness perspective, of showing we don't have a lot of thought diversity. We have a great opportunity to increase that, and you've got a great platform in order to share your story. >> Yeah. Well, I think that you raise a good point though, as, even though the number of women majoring in STEM fields, at least in some areas of STEM has increased, the number of women making it higher up in the STEM ladder hasn't, for the most part. And one reason for this is possibly the lack of female role models. So being able to attend a conference like this, for young women who are interested in developing their career in STEM, I'm sure is really inspirational and a great opportunity. So it's wonderful for Margot and the other organizers to have put this together. >> It is. Even on the recruiting side, some of the things that still surprise me are when some, whether it's universities or companies that are going to universities to recruit for STEM roles, they're still bringing mostly men. And if there are females at the events, they're, often times they're handing out swag, they're doing more event coordination, which is great. I'm a marketer. There's a lot of females in marketing. But it still shows the need to start from a visibility standpoint and a messaging standpoint alone. They've got to flip this. >> I completely agree with that, but it also works the other way. So, often a company or an academic department might have a few women in a particular role, and those women get asked to do everything. Because they'll say, "Oh, we're going to Stanford to recruit. We need a woman there. We're having some event, and we don't want it to look totally non-diverse, so we need a woman there too." And the small number of women in STEM get asked to do a lot of things that the men don't get asked to do, and this can also be really problematic. Even though the intent is good, to clearly showcase the fact that there's diversity in STEM and in academia, the end outcome can actually be hurtful to the women involved who are being asked to do more than their fair share. So we need to find a way to balance this. >> Right. That balance is key. So what I want to kind of pivot on next is, just looking at the field of data science, it's so interesting because it's very, I like 'cause it's horizontal. We just had a guest on from Uber, and we talk to on The Cube, people in many different industries, from big tech to baseball teams and things like that. And what it really shows, though, is, there's blurred lines, or maybe even lines that have evaporated between demarcated career A, B, C, D. And data science is so pervasive that it's impacting, people that are working in it, like yourself, have the ability to impact every sector, policy changes, things like that. Do you think that that message is out there enough? That the next generation understands how much impact they can make in data science? >> I think there is a lot of excitement from young people about data science. At U-dub, we have a statistics major, and it's really grown a lot in popularity in the last few years. We have a new master's degree in data science that just was started around the same time that WIDS was started, and we had 800 applicants this year. >> Wow. >> For a single masters program. Truly incredible. But I think that there's an element of it that also maybe people don't realize. So data science, there's a technical skill set that comes with it, and people are studying undergrad in statistics, and getting master's in data science in order to get that technical skill set. But there's also a non-technical skill set that's incredibly important, because data science isn't done in a vacuum. It's done within the context of interdisciplinary teams with team members from all different areas. So, for example, in my work, I work with biologists. Your previous guest from Uber, I'm sure is working with engineers and all different areas of the company. And in order to be successful in data science, you need to really not only have technical skills, but also the ability to work as a team player and to communicate your ideas. >> Yeah, you're right. Balancing those technical skills with, what some might call soft skills, empathy, collaboration, the ability to communicate, seems to be, we talked about balance earlier, a scale-wise. Would you say they're pretty equivalent, in terms of really, that would give somebody a great foundation as a data scientist? >> I would say that having both of those skill sets would give you a good foundation, yes. The extent to which either one is needed probably depends on the details of your job. >> True. So, I want to talk a little bit more about your background. Something that caught my eye was that your work has been featured in popular media. Forbes, three times, and Elle magazine, which of course, I thought, "What? I've got to talk to you about that!" Tell me a little bit about the opportunities that you've had in Forbes and in Elle magazine to share your story and to be a mentor. >> Yeah. Well, I've just been lucky to be getting involved in the field of statistics at a time when statistics is really growing in importance and interest. So the joke is, that ten years ago, if you went to a cocktail party, and you said that you were a statistician, then nobody would want to talk to you. (Lisa laughs) And now, if you go to a cocktail party and you say you're a statistician, everyone wants to know more and find out if you know of any job openings for them. >> Lisa: That's pretty cool! >> Yeah. So it's a really great time to be doing this kind of work. And there's really an increased appreciation for the fact that it's not enough to have access to a lot of data, but we really need the technical skills to make sense of that data. >> Right. So share with us a little bit about the session that you're doing here: More Data, More Statistical Problems. Tell us a little bit about that and maybe some of the three, what are the three key takeaways that the audience was hearing from you? >> Yeah. So I think the first real takeaway is, sometimes there's a feeling that, when we have a lot of data, we don't really need a deep understanding of statistics, we just need to know how to do machine learning, or how to develop a black box predictor. And so, the first point that I wanted to make is that that's not really right. Actually, the more data you have, often the more opportunity there is for your analysis to go awry, if you don't really have the solid foundations. Another point that I wanted to make is that there's been a lot of excitement about the promise of biology. So, a lot of my work has biomedical applications, and people have been hoping for many years that the new technologies that have come out in recent years in biology, would lead to improve understanding of human health and improve treatment of disease. And, it turns out, that it hasn't, at least not yet. We've got the data, but what we don't know how to do is how to analyze it yet. And so, the real gap between the data that we have and achieving its promise is actually a statistical gap. So there's a lot of opportunity for statisticians to help bridge that gap, in order to improve human health. And finally, the last point that I want to make is that a lot of these issues are really subtle. So we can try to just swing a hammer at our data and hope to get something out of it, but often there's subtle statistical issues that we need to think about, that could very much affect our results. And keeping in mind sort of the effects of our models, and some of these subtle statistical issues is very important. >> So, in terms of your team at University of Washington, or your classes that you teach, you work with undergrads. >> Yeah, I teach undergrads and PhD students, and I work mostly with PhD students. And I've just been lucky to work with incredibly talented students. I did my PhD here at Stanford, and I had a great advisor and really wonderful mentoring from my advisor and from the other faculty in the department. And so it's really great to have the opportunity now, in turn, to mentor grad students at University of Washington. >> What are some of the things that you help them with? Is it, we talk about inspiring women to get into the field, but, as you prepare these grad students to finish their master's or PhD's, and then go out either into academia or in industry, what are some of the other elements that you think is important for them to understand in terms of learning how to be assertive, or make their points in a respectful, professional way? Is that part of what you help them understand and achieve? >> That's definitely part of it. I would say another thing that I try to teach them, so everyone who I work with, all my students, they're incredibly strong technically, because you don't get into a top PhD program in statistics or biostatistics if you're not technically very strong, so what I try to help my students do is figure out not just how to solve problems, because they can solve any problem they set their mind to, but actually how to identify the problems that are likely to be high impact. Because there's so many problems out there that you can try to solve statistically, and, of course, we should all be focusing our efforts on the ones that are likely to have a really big impact on society, or on health, or whatever it is that we're trying to influence. >> Last question for you. If you look back to your education to now, what advice would you give your younger self? >> Gosh, that's a really great question. I think that I'm happy with many of the career decisions I've made. For example, getting a PhD in statistics, I think is a great career move. But, at the same time, maybe I would tell a younger version of me to take more risks, and not be so worried about meeting every requirement on time, and instead, expanding a little bit, taking more courses in other areas, and really broadening instead of just deepening my skill set. >> We've heard that sentiment echoed a number of times today, and one of the themes that I'm hearing a lot is don't be afraid to get out of your comfort zone. And it's so hard for us when we're in it, when we're younger, 'cause you don't know that, you don't have any experience there. But it's something that I always appreciate hearing from the women who've kind of led the way for those of us and then, the next generation, is, don't be afraid to get comfortably uncomfortable and as you said, take risks. It's not a bad thing, right? Well, Daniela, thanks so much for carving out some time to visit us on The Cube, and we're happy to have given you the opportunity to reach an even bigger audience with your message, and we wish you continued success at U-dub. >> Oh, thanks so much. >> We want to thank you for watching. I'm Lisa Martin live with The Cube at WIDS 2018 from Stanford University. Stick around, I'll be back with my next guest after a short break. (energetic music)

(light electronic music) >> Announcer: Live from Stanford University, in Paolo Alto, California, it's theCUBE. Covering Women in Data Science Conference, 2018. Brought to you by Stanford. >> Welcome back to theCUBE, I'm Lisa Martin. At Stanford University, we're here for the third annual Women in Data Science Conference. #WiDS2018, check it out, be part of the conversation, WiDS is in it's third year, but it's aiming to reach about a hundred thousand people this week alone. There's 177 regional WiDS events in 53 countries. This event here, the main event at Stanford, features key notes, technical vision talks, a career panel, and we're excited to be joined next by Dr. Nathalie Henry Riche. I did that in French. >> Yes. (laughs) Who is a researcher at Microsoft, and Natalie, first of all, welcome to theCUBE. >> Thank you, I'm really thrilled to be here. >> Yeah, you gave a technical vision talk on data visualization, and data driven's story telling. Share with our audience, some of the key messages, that the WiDS audience heard from you earlier today. >> Well, I guess, I gave two main messages. The first one is, that a visualization has two superpowers. >> Lisa: Superpowers? >> Superpowers. >> Tell me girl. The first one is enable you to kind of think about your data in a new way. So, just kind of form hypothesis, and answer questions you didn't even know, you had by your data. So, that's the first one. The second super power, is it's really useful to communicate information, and communicate with a large audience. Visualization helps you, kind of convey your point with data, to back it up. So, that's kind of the short one minute. >> I love that, super super hero, super power. So, WiDS is, as I mentioned at the intro, in its third year, and reaching, it's grown dramatically in such a short period of time. This is your first WiDS, and your first WiDS you are a speaker. What was is that attracted you to WiDS, and you went, yes I want to give some of my time to this, and come down from Seattle? >> Well, so I'm French originally, and my studies I did at engineering school, and it was one of three out of 300 men, right? >> Wow. >> So, I was requested a lot for women in computer science, and engineering. So, I actually really like it. Just meeting all of those people, talking about, you know, trying to bring more women in. Part of the job I'm doing is very creative, so, we're trying to come up with new ideas for visualization. I think having, you know, a wide range of people adds to the mix, and we get so many more exciting ideas. So, I really want to try to have more diverse group of people I can work with, and connect to, and so that's why that attracted me to here. >> Excellent, couple of things that you said I've heard a number of times today. The first one is, what Daniela went and shared, who's also a speaker, that often times, some of the few women in tech, and you mentioned being one of three in 300? Are asked to do a lot of other things. Did you find that, that, okay you're one of the few females, you're articulate, you like speaking, we want you to do all these things. >> Yes, and I say no a lot. (laughs) >> 'Cause I have kids, too. >> That's a skill, too. But yeah, it happens a lot. I think as we go further, it's going to be less and less happening. It's better in the end. So, it's kind of a service, I see it as a service to, you know, my field, and my company. But, at the same time, we'll also get a lot of benefits from it. But that said, I try to cut it down to a manageable level, so two hours flight from Seattle works great. >> Right, right, right. Another thing is that, that you mentioned the creativity. I've heard that a number of times, today from our guest Margot Gerritsen, was on as well. Tell me about your thoughts about being in this data science role, the need for creativity. How does, how it, why is that you might consider it, like a softer skill versus the technical skills. But, how important is that creativity in your job, for example? >> So, my job is really like researcher. Trying to have new ideas, and innovate for Microsoft in particular. So, I'm not really a data scientist, but I build the tools for a data scientist. So, knowing that, creativity is important because you need to kind of think out of the box. What is the next generation of tools that they will need? In turn, they need to think out of the box, kind of get more insight out of the data they're collecting. So, creativity is just like, pervasive to this whole data science thing. Problem solving as well, so you need a lot the left brain, and a lot of the right brain. Kind of both of them together. I think that having different cultures, and different genders, even different age ranges just, you know, makes you think out of the box. That's just what's happening. Discussing with people, I was discussing with someone in cosmology, and I was like, whoa. That brought up a lot of different ideas in me, so, to me, that's really critical part of what I'm doing every day. >> I like that, that kind of aligns to what one of our guests said earlier, and that is the thought diversity. Wow, I've never >> Yes. thought of thought diversity. But, you bring up a good point about it's not just about having women in the field, it's also having diversity, in terms of generations. One of the things that's, I think, pretty unique about WiDS, is it's not just about reaching young women in their first semester at University, for example. Maria Clavijo said that's the ideal time to really inspire. But, it's also reinvigorating women who've been in academia, or industry in stem subjects for a long time. So, you have, we have multiple generations, and to your point, that diversity is important, it's not just about gender, ethnicity. It's also about the diverse perspectives that come from being >> Exactly. from different generations. >> So, it's funny, 'cause I was giving this talk earlier, and it was, one part of it was about time line. When I was researching, you know how people draw time? Well there's, depending some culture, it goes from left to right, but some other culture it's front to back, back to front, right to left. So, we need to be aware of all of that, and it's so much easier to just have the people to converse with right in your office, or next door, to be aware of those. So, that's very important, especially to big companies, like Microsoft, 'cause of, you know, a lot of customers world wide. So, it's very important to just be immersed in that. >> Definitely. So, you have been published, you've got published research, and over 60 articles in leading venues, and human-computer interaction, and information visualization. But, something we chatted about off camera, was very intriguing about visualization and children. Tell me a little bit more about that. >> So, I happen to have two kids, you know, seven and four. I'm passionate about what I'm doing, and I just couldn't keep it out of their hands, right? So, I was just starting, you know, seeing what does my daughter learn at school, like, what does she learn in kindergarten? In fact, in kindergarten, I remember one day, she brought back candies, and I'm like did you get candies from school? She's like no, because we were doing a bar chart. I was like, what? (laughs) So, I was very intrigued in, you know, what do we teach, what do your kids learn? It was fascinating to see that, you know, from an early age, they learn how to do those visualizations. But, they don't really learn how you can lie with them, or you know, to kind of think critically about that. That, you know, maybe you can start your bar chart at two, and you know, you would have less candy, I guess. But, you could, kind of convey the wrong messages. So, I became passionate about this, and decided we need to just improve our teaching about how we can represent data, and how we can also misrepresent it. In the hope that for the next generation to come, they'll be able to look at a chart, and think critically about it. Whether or not it tells the right story with the right data. Kind of beyond, just picture's worth a thousand words, then I'm not going to think about it. >> Yeah. >> This is kind of my personal effort that I try to move myself forward. (chuckles) >> Well, it's so important about having that passion, and I think that's one of things that seems to be inherent about WiDS. Even, you know, yesterday seeing on the Twitter stream, WiDS New Zealand starting in five minutes, and it's been really focused on being so, kind of inclusive. Just sort of naturally, and one of the things that I learned in some of my prep for the show, is the bias that is still there, in data interpretation. You kind of talked about that, and I never really thought about it in that way. But, if a particular group of people is looking at a data set, and thinking it says this, and no other opinions, perspectives, thoughts are able to be incorporated to go, well, maybe it says this. >> Yeah. >> Then we're limiting ourselves in terms of one, the potential that the data has to, you know, help a business, create a new business model. But also, we're limiting our perspectives on making a massive social impact with data. >> Yeah, what I find very interesting is visualization often people think about it at the end of the spectrum. Like, I've collected my data, I analyze it, and now I need to pretty picture to kind of explain what I found. But, the most powerful use of visualization, I think, comes early on. Where you actually just collected your data, and you look at it before you run any statistical test. I did that not long ago with French air traffic data in the Hollands, I put them in, and I saw the little airplanes moving around. Then, what we saw, is one air planes doing loops like this. I was like, what is this going on, right? It was just a drone, doing like tests, right? But, somehow it got looped in into that data set. So, by looking at your data early on, you can detect what's wrong with the data. So then, when you actually run your statistical test, and your analysis, you better reflect what was that data in the first place, you know, what could go wrong there? So, I think inserting visualization early on is also critical to understand what we can really know, and do, and ask, about the data in the first place. >> So, it's kind of like, watching the story unfold, rather than going, we've done all this analysis here's the picture, the story is this. The story is, your sort of, turning it sort of page by page, it sounds like, and watching it, and interpreting it, as it's unfolding. >> Rethinking what you collected in the first place. Is that the right data you collected to answer the question you wanted to ask? Is it a good match or not? Then, rethink that, you know, collect new data, or the missing one, and then go on with your analysis. So, I think to me, it's really a thinking tool. >> It also sounds like another, we talked about the technical skills that had, obviously that a computer scientist, data scientist needs to have. But, there's other skills. Empathy, communication, collaboration. Sounds like also, there needs to be an ideal kind of skill set, it has to include open mindedness. >> Yes. >> Tell me a little bit about some of your experiences there, and not being married to, the data must say this. So, if it doesn't, I'm not going to look anywhere else. Where is open mindedness, in terms of being a critical skill set that needs to come to the field? >> Yeah, I mean we, that's that is totally a re-critical point. Think already, when you're collecting the data, especially as a scientist, when I run experiment, I kind of know what I want to find. Sometimes, you don't find it. You need to kind of embrace it. But, it's hard to have because sometimes, it's like those unconscious bias you have. Like, you're not really necessarily controlling them, and just the way you collected the data in the first place, maybe just, you know, skewed your result. So, it's very important to kind of think ahead of time of all of those bias you could have, and think about all of what could go wrong. Often, the scientific process is actually that trying to think about all of the stuff that could go wrong, and then check whether or not they're wrong. We're trying to infuse that, a little bit over Microsoft as well, kind of, you know, the data that we collect, can we analyze them, can we have teams of people who really think is that the right data? Are we collecting like, world-wide for example? Are we just collecting from the US? So, there's a lot of those, kind of, ethical, and bias, kind of training, and effort to try and remove that. The maximum from our work, and I think that it's across the entire world. I think, with all of this data collection everywhere, we kind of have to do that, very consciously. >> I think two things kind of speak to me that out of what you just said, that we've heard a number of times today. One, that failure, and I don't mean to say that failure is not a bad thing. That's how you, >> That's how you learn, Exactly, >> and grow. Exactly, in many ways it's not a bad F-word, it's this is how everybody that's successful got to wherever they are. But, it's also about embracing, as you said, the word embracing, embracing the fact that you might be bring bias into this, and you have to be okay with maybe this is the wrong data set. If you consider that a failure, consider it, to your point, a growth opportunity. That is one of the themes that we've heard today, and you've, kind of, elaborated on that. The second one is, be okay getting uncomfortable, get out of that comfort zone. Consciously uncomfortable, because when you're able to do that, the possibilities are limitless. >> Yes, and that's what I try to do everyday, 'cause I try to push all of the software that we're doing, and Microsoft is so big, you know, and all of those software are like so there. (laughs) So trying to come up with new ideas, like so many are failures, you know. Oh they won't make money, or they don't actually work when you, you know, for this population. So, most of my work is failure. (laughs) But hey, one success when you know why, and I'm happy about it. >> Exactly, but it's just charting that course to getting to the ah, this is the pot of gold at the end of the rainbow. Well Nathalie, thank you so much for taking some time to talk with us on theCUBE, and sharing your stories. Congratulations on being a speaker, your first WiDS, and we look forward to seeing you back next year. >> Thank you very much. >> We want to thank you for watching theCUBE. I'm Lisa Martin, live from WiDS 2018 at Stanford University. Stick around, I'll be back with my next guest after a short break. (light electronic music)

>> Narrator: Alumni. (upbeat music) >> Announcer: Live from Stanford University in Palo Alto, California, it's theCUBE. Covering Women in Data Science Conference 2018. Brought to you by Stanford. >> Welcome back to theCUBE, we are live at Stanford University for the third annual Women in Data Science Conference, WiDS. I'm Lisa Martin, very honored to be joined by one of the co-founders of this incredible WiDS movement and phenomenon, Dr. Margot Gerritsen. Welcome to theCUBE! >> It's great to be here, thanks so much for being at our conference. >> Oh, likewise. You were the senior associate dean and director of the Institute for Computational Mathematics and Engineering at Stanford. >> Gerritsen: That's right, yep. >> Wow, that's a mouthful and I'm glad I could actually pronounce that. So you have been, well, I would love to give our audience a sense of the history of WiDS, which is very short. You've been on this incredible growth and scale trajectory. But you've been in this field of computational science for what, 30, over 30 years? >> Yeah, probably since I was 16, so that was 35 years ago. >> Yeah, and you were used to being one of few, or if not the only woman >> That's right. >> In a meeting, in a room. You were okay with that but you realized, you know what? There are probably women who are not comfortable with this and it's probably going to be a barrier. Tell us about the conception of WiDS that you and your co-founders had. >> So, May, 2015, Esteban from Walmart Labs, now at Facebook, and Karen Matthys, who's still very active, you know, one of the organizers of the conference, and I were having coffee at a cafe in Stanford and we were lamenting the fact that at another data science conference that we had been to had only had male speakers. And so we connected with the organizers and asked them why? Did you notice? Because very often people are not even aware, it's just such the norm to only have male speakers, >> Right, right. >> That people don't even notice. And so we asked why is that? And they said, "Well, you know we really tried to find "speakers but we couldn't find any." And that really was, for me, the last straw. I've been in so many of these situations and I thought, you know, we're going to show them. So we joke sometimes, a little bit, we say it's sort of a revenge conference. (laughs) We said, let's show them we can get some really outstanding women, and in fact only women. And that's how it started. Now we were sitting at this coffee shop and I said, "Let's do a conference." And they said, "Well, that would be great, next year." And I said, "No, this year. "Let's just do it. "Let's do it in November." We had six months to put it together. It was just a local conference here. We got outstanding speakers, which were really great. Mostly from the area. And then we started live-streaming because we thought it would be fun to do. And to our big surprise, we had 6,000 people on the livestream just without really advertising. That made us realize, in November 2015, my goodness, we're onto something. And we had such amazing responses. We wanted to then scale up the conference and then you can hire a fantastic conference center in San Francisco and get 10,000 people in like they do, for example, at Grace Hopper. But we thought, why not use online technology and scale it up virtually and make this a global event using the livestream, that we will then provide to people, and asking for regional events, local events to be set up all around the world. And we created this ambassador program, that is now in its second year. the first year the responses were actually overwhelming to us already then. We got 75 ambassadors who set up 75 events around the world >> In about 40 countries. >> This was last year, 2017? >> Yeah, almost exactly 13 months ago, and then this year now we have over 200 ambassadors. We have 177 events in 155 cities in 53 countries. >> That's incredible. >> So we're on every continent apart from Antarctica but we're working on that one. >> Martin: I was going to say, that's probably next year. >> Yeah, that's right. >> The scale, though, that you've achieved in such a short time period, I think, not only speaks to the power, like you said, of using technology and using live-streaming, but also, there is a massive demand. >> Gerritsen: There is a great need, yeah. >> For not only supporting, like from the perspective of the conference, you want to support and inspire and educate data scientists worldwide and support females in the field, but it really, I think, underscores, there is still in 2018, a massive need to start raising more profiles and not just inspiring undergrad females, but also reinvigorating those of us that have been in the STEM field and technology for a while. >> Gerritsen: That's right. >> So, what are some of the things, so, this year, not only are you reaching, hopefully about 100,000 people, you mentioned some of the countries involved today, but you also have a new first this year with the WiDS Datathon. >> That's right. >> Tell us about the WiDS Datathon, what was the idea behind it? You announced some winners today? >> Yeah. Yeah, so with WiDS last year, we really felt that we hit a nerve. Now there is an incredible need for women to see other women perform so well in this field. And, you know, that's why we do it, to inspire. But it's a one-time event, it's once a year. And we started to think about, what are some of the ways that we can make this movement, because it's really become a movement, into something more than just an annual, once-a-year conference? And so, Datathon is a fantastic way to do that. You can engage people for several months before the conference, and you can announce the winner at the conference. It is something that can be done really easily worldwide if it is supported again by the ambassadors, so the local WiDS organizations. So we thought we'd just try. But again, it's one of those things we say, "Oh, let's do it." We, I think, thought about this about six months ago. Finding a good data set is always a challenge but we found a wonderful data set, and we had a great response with 1100, almost 1200 people in the world participating. >> That's incredible. >> Several hundred teams. Yeah, and what we said at the time was, well, let's have the teams be 50% female at least, so that was the requirement, we have a lot of mixed teams. And ultimately, of course, that's what we want. We want 50-50, men-women, have them both at the table, to participate in data science activities, to do data science research, and answer a lot of these data questions that are now driving so many decisions. Now we want everybody around the table. So with this Datathon, it was just a very small event in the sense, and I'm sure next year it will be bigger, but it was a great success now. >> Well, congratulations on that. One of the things I saw you on a Youtube video talking about over the weekend when I was doing some prep was that you wanted this Datathon to be fun, creative, and I think those are two incredibly important ways to describe careers, not just in STEM but in data science, that yes, this can be fun. >> Yep. >> Should be if you're spending so much time every day, right, doing something for a living. But I love the creativity descriptor. Tell us a little bit about the room for interpretation and creativity to start removing some of the bias that is clearly there in data interpretation? >> Oh. (laughs) You're hitting the biggest sore point in data science. And you could even turn it around, you say, because of creativity, we have a problem too. Because you can be very creative in how you interpret the data, and unfortunately, for most of us, whenever we look at news, whenever we look at data or other information given to us, we never see this through an objective lens. We always see this through our own filters. And that, of course, when you're doing data analysis is risky, and it's tricky. 'cause you're often not even aware that you're doing it. So that's one thing, you have this bias coming in just as a data scientist and engineer. Even though we always say we do objective work and we're building neutral software programs, we're not. We're not. Everything that we do in machine learning, data mining, we're looking for patterns that we think may be in the data because we have to program this data. And then even looking at some of the results, the way we visualize them, present them, can really introduce bias as well. And then we don't control the perception of people of this data. So we can present it the way we think is fair, but other people can interpret or use little bits of that data in other ways. So it's an incredibly difficult problem and the more we use data to address and answer critical challenges, the more data is influencing decisions made by politicians, made in industry, made by government, the more important it is that we are at least aware. One of the really interesting things this conference, is that many of the speakers are talking to that. We just had Latanya Sweeney give an outstanding keynote really about this, raising this awareness. We had Daniela Witten saying this, and various other speakers. And in the first year that we had this conference, you would not have heard this. >> Martin: Really? Only two years ago? >> Yeah. So even two years ago, some people were bringing it up, but now it is right at the forefront of almost everybody's thinking. Data ethics, the issue of reproducibility, confirmations bias, now at least people now are aware. And I'm always a great optimist, thinking if people are aware, and they see the need to really work on this, something will happen. But it is incredibly important for the new data scientists that come into the field to really have this awareness, and to have the skill sets to actually work with that. So as a data scientist, one of the reasons why I think it's so fun, you're not just a mathematician or statistician or computer scientist, you are somebody who needs to look at things taking into account ethics, and fairness. You need to understand human behavior. You need to understand the social sciences. And we're seeing that awareness now grow. The new generation of data scientists is picking that up now much more. Educational programs like ours too have embedded these sort of aspects into the education and I think there is a lot of hope for the future. But we're just starting. >> Right. But you hit the nail on the head. You've got to start with that awareness. And it sounds like, another thing that you just described is we often hear, the top skills that a data scientist needs to have is statistical analysis, data mining. But there's also now some of these other skills you just mentioned, maybe more on the softer side, that seem to be, from what we hear on theCUBE, as important, >> Gerritsen: That's right. >> As really that technical training. To be more well-rounded and to also, as you mentioned earlier, to have to the chance to influence every single sector, every single industry, in our world today. >> And it's a pity that they're called softer skills. (laughs) >> It is. >> Because they're very very hard skills to really master. >> A lot of them are probably you're born with it, right? It's innate, certain things that you can't necessarily teach? >> Well, I don't believe that you cannot do this without innate ability. Of course if you have this innate ability it helps a little, but there's a growth mindset of course, in this, and everybody can be taught. And that's what we try to do. Now, it may take a little bit of time, but you have to confront this and you have to give the people the skills and really integrate this in your education, integrate this at companies. Company culture plays a big role. >> Absolutely. >> This is one of the reasons why we want way more diversity in these companies, right. It's not just to have people in decision-making teams that are more diverse, but the whole culture of the company needs to change so that these sort of skills, communication, empathy, big one, communication skills, presentation skills, visualization skills, negotiation skills, that they really are developed everywhere, in the companies, at the universities. >> Absolutely. We speak with some companies, and some today, even, on theCUBE, where they really talk about how they're shifting, and SAP is one of them, their corporate culture to say we've got a goal by 2020 to have 30% of our workforce be female. You've got some great partners, you mentioned Walmart Labs, how challenging was it to go to some of these companies here in Silicon Valley and beyond and say, hey we have this idea for a conference, we want to do this in six months so strap on your seatbelts, what were those conversations like to get some of those partners onboard? >> We wouldn't have been able to do it in six months if the response had not been fantastic right from the get-go. I think we started the conference just at the right time. There was a lot of talk about diversity. Several of the companies were starting really big diversity initiatives. Intel is one of them, SAP is another one of them. We were connected with these companies. Walmart Labs, for example, one of the founders of the company was from Walmart Labs. And so when we said, look, we want to put this together, they said great. This is a fantastic venue for us also. You see this with some of these companies, they don't just come and give us money for this conference. They build their own WiDS events around the world. Like SAP built 30 WiDS events around the world. So they're very active everywhere. They see the need, of course, too. They do this because they really believe that a changed culture is for the best of everybody. But they also believe that because they need the women. There is a great shortage of really excellent data scientists right now, so why not look at 50% of your population? >> Martin: Exactly. >> You know, there's fantastic talent in that pool and they want to track that also. So I think that within the companies, there is more awareness, there is an economic need to do so, a real need, if they want to grow, they need those people. There is an awareness that for their future, the long term benefit of the company, they need this diversity in opinions, they need the diversity in the questions that are being asked, and the way that the companies look at the data. And so, I think we're at a golden age for that now. Now am I a little bit frustrated that it's 2018 and we're doing this? Yes. When I was a student 30 some years ago, I was one of the very few women, and I thought, by the time I'm old, and now I'm old, you know, as far as my 18-year-old self, right, I mean in your 50s, you're old. I thought everything would be better. And we certainly would be at critical mass, which is 30% or higher, and it's actually gone down since the 80s, in computer science and in data science and statistics, so it is really very frustrating in that sense that we're really starting again from quite a low level. >> Right. Right. >> But I see much more enthusiasm and now the difference is the economical need. So this is going to be driven by business sense as well as any other sense. >> Well I think you definitely, with WiDS, you are beyond onto something with what you've achieved in such a short time period. So I can only imagine, WiDS 2018 reaching up to 100,000 people over these events, what do you do next year? Where do you go from here? (laughs) >> Well, it's becoming a little bit of a challenge actually to organize and help and support all of these international events, so we're going to be thinking about how to organize ourselves, maybe on every continent. >> Getting to Antarctica in 2019? >> Yeah, but have a little bit more of a local or regional organization, so that's one thing. The main thing that we'd like to do is have even more events during the year. There are some specific needs that we cannot address right now. One need, for example, is for high school students. We have two high school students here today, which is wonderful, and quite a few of them are looking at the live-stream of the conference. But if you want to really reach out to high school students and tell them about this and the sort of skill sets that they should be thinking about developing when they are at university, you have to really do a special event. The same with undergraduate students, graduate students. So there are some markets there, some subgroups of people that we would really like to tailor to. The other thing is a lot of people are very very eager to self-educate, and so what we are going to be putting together, at least that's the plan now, we'll see, if we can make this, is educational tools, and really have a repository of educational tools that people can use to educate themselves and to learn more. We're going to start a podcast series of women, which will be very, very interesting. We'll start this next month, and so every week or every two weeks we'll have a new podcast out there. And then we'll keep the momentum going. But really the idea is to not provide just this one day of inspiration, but to provide throughout the year, >> Sustained inspiration. >> Sustained inspiration and resources. >> Wow, well, congratulations, Margot, to you and your co-founders. This is a movement, and we are very excited for the opportunity to have you on theCUBE as well as some of the speakers and the attendeees from the event today. And we look forward to seeing all the great things that I think are going to come for sure, the rest of this year and beyond. So thank you for giving us some of your time. >> Thank you so much, we're a big fan of theCUBE. >> Oh, we're lucky, thank you, thank you. We want to thank you for watching theCUBE. I'm Lisa Martin, we are live at the third annual Women in Data Science Conference coming to you from Stanford University, #WiDS2018, join the conversation. I'll be back with my next guest after a short break. (upbeat music)