 Live from Stanford University, it's theCUBE, covering the Women in Data Science Conference 2017. Hi, welcome to theCUBE, I'm Lisa Martin, and today we are at Stanford University, the second annual Women in Data Science Conference. This is a phenomenal event. This is our second time here. John Furrier is my co-host, John. Tell us about theCUBE's involvement and your involvement in the Women in Data Science. Lisa, great to see you, I'm excited to be here. We got the live broadcast, certainly it's an exciting event. Women in Data Science, this is the second event. The inaugural event was last year or just almost 13 months ago, Judy Logan and Karen were running this. And it was really about getting women who were super geeks, I call them, who were really doing like programming and hardcore data science work together. And it's now grown into a worldwide phenomenon as we've been seeing in the world these days. The women are uniting and what's exciting is around, it's around something cool like tech, right? So not just you seeing on the political side the movements going on, but the Women in Data Science really is what I call the tech athlete, the inner geeks who are smart and doing great stuff. And this event has grown from baselining here at Stanford University to all around the world. So if folks are gathering 400 at a time and hundreds of thousands of people all around the world are gathering to peer up. And it's been a great event. We've been involved at the beginning of the event with theCUBE and again, it's part of our passion with women in tech and women in data science and the Grace Hopper and all the variety of events. As you know, we do and surfacing new voices is really our core mission. And I think this is the kind of event that shows that these communities are surfacing up new voices to the world, not the same old trot and doubt people who are saying the same things over and over again. There's now this community involvement where real smart people are coming together because they're all connected. So there's a positive element around being connected. So I love it. It's really at an inflection point. You mentioned attendees last year, a little over a year ago was the first annual with about 400 attendees and they livestreamed this. This is being livestreamed to over 75 locations across the country. In fact, this event's been sold out for weeks. One of the things that's really interesting, John, is in the second year, Diane Greene, the SVP of Google Cloud is the morning keynote. You were in her keynote. Talk to us about Diane Greene, Google Cloud and really some of the interesting advice that she has given the audience this morning. Yeah, it's super exciting to be also as reporting on the event, Diane Greene was one of the marquee keynotes. Honestly, she is on the board of directors of Alphabet, which is the Google now mega company. She's been on it for multiple years as a board member, but she's also a Silicon Valley legend. I mean, in my mind, she's up there with some of the big names like Steve Jobs and the Andy Grove kind of names because she's been a geek all of her life and she told her story about when she was on a beach literally in Hawaii doing windsurfing and power sailing and how she's always had an attraction to engineering, had a degree at MIT and then went to Cal for her master's degree in computer science. But she took us through her journey to the founding of VMware and how she had her first child and second child during those 10-year startup run there. And it was exciting, but the key message was that, you know, look at, there's so much opportunity around data science and she said I thought was some very interesting quotes was one was that machine learning is going to take away jobs and that's a fact, there's no debate about it. She said that directly as a matter of fact that was her direct quote and then she said, but society has to figure out how we replace those jobs and provide a path. The other thing that she said was machine learning is the new revolution and with all the hype around artificial intelligence or AI and self-driving cars, it's really the cloud computing paradigm and AI and machine learning at the center of it as the revolution. So cloud was revolution one and revolution two going on now in the tech business that's impacting all these wonderful women in tech and others is machine learning. So in the data science field, machine learning is becoming the kernel of the center of this revolution because there's unlimited potential compute power available, the software being written around data sets are available and the data sets themselves are now available. So Google Cloud's doing things for companies and their customers like Spotify for instance, doing things that used to be in the database world take 14 hours now in 16 seconds. So that was a huge thing. Another thing about Diane Greene's keynote that I thought was fascinating was that she's a data geek at heart and a lot of folks might not know this but she was an angel investor in Cloudera and Cloudera is where theCUBE was founded. That was our first office sharing with them when they had like 17 employees. They became the pioneer for Big Data and Hadoop. She was an early investor there. Her husband as a professor at Stanford, she's in the roots. But Diane Greene really is one of those executives that doesn't get a lot of press because she's not actually looking for it, right? She's just super humble and big fan of hers. And again, I think she's one of the big tech stories that really hasn't been written yet. So we're looking forward to following up with her and telling the Google Cloud story with Diane Greene. Absolutely. She gave some interesting career advice. Can you share a little bit about that? Yeah, so the question always comes up and one question here was, what advice do you give for someone who's in the data science? And she kind of took a step back and she's got these mannerisms that's like, I think she wanted to say something else but I think she kind of paused because it's really, it's a hard answer. What do you start? Because it kind of depends. Data science really comes down to where your passion is, but she said this. She said, if you go deep on something and she used her past as an engineering degree, go deep on something. Because if you can go deep on something and prove that you can do something rigorous, whether it's engineering, a physics or a degree, once you go deep, then you can then look at some horizontal opportunities and you can probably work on any project. So her advice was, try to go deep and be rigorous and disciplined in something that you can become an expert in. But that can translate to other projects. So that was one. The other one was, don't be afraid to take a risk because she was talking about her personal experiences where she's always worried about getting fired. And she said, don't worry about the little things because we're in the data science field. If you get fired, you can get hired right away. So she's kind of giving like a pep talk. I thought that was very good advice. That's fantastic advice because you don't hear that. John and I were talking off camera. It's from the generations that we grew up and obviously very different than the younger generation today, but that was not advice that you and I were probably ever given. It was, you get a job and you do that until you retire. So hearing somebody as successful and a female as Diane Green saying, don't be afraid to get fired is phenomenal advice. But it comes from experience. You talk about, as she did, the demand for data scientists is huge. In fact, it's predicted to be a shortage of talent in the next year. How do you see events like women in data science helping to combat that and really inspire the next generation? You know, I was just talking with Dave Vellante yesterday about this and our team's working on this thing called theCUBE 365, which is trying to take the magic of going and taking the live broadcast that we are now and sharing information to making it a digital product. And this is important because the trend that's going on in this event here that we see as a success formula is that there's an origination of an event like at Stanford or wherever and they create a community aspect to it. And what's going on is that this seems to be the new e-learning environment. So right now you have two ways to learn, right? You have the old way, which is going to school, going to classes, or maybe going to online, some email blast that you get and you kind of respond to it and then the other new way is the old way that's new old is e-learning catalogs. You sign up for a course, you go to Coursera or with these sites and you sit down there and you go through some linear catalog and you say, I pass 101, I go to 102. So the e-learning environments are not necessarily optimized for what we see as the key learning dynamic, which is social interaction. So there's a new dynamic developing and that's where machine learning and AI are seeming to augment that is that it's not about school online, it's about people who are smart together having hallway-like conversations where the acceleration of learning is done with peer groups and peer interactions. So what we're seeing on Twitter and Facebook is some of these new environments where as content gets shared, like we're doing here on theCUBE, that fosters a discovery and a progression to learn, to some proficiency and that's developing as a whole new category of learning. Not, I got a degree online or I went to a class. Maybe the combination of all three, but it's a progression to proficiency. This is a new dynamic. That's one of the things I'm particularly interested in about and talking to some of our guests, whether they're leaders in retail or e-commerce or educators is the data science traditional skills, do you have to be a hacker or you hybrid hacker, math, statistician? But now what's emerging and you just kind of touched on this is the social skill impact. Being good at being able to analyze data sets is great, but you have to be able to communicate that. And apply them. Absolutely. And this is a great, you'd say the word network of very inspiring women and men as well who are helping to really transform those hard skills as this revolution is evolving the social components. If you think about the learning, Lisa this is interesting conversation because you think of the learning is that you think about learning and if you look at what works and there's a couple of different things. When you think about agile software development or being agile, agile is also in part of the learning. So if you look at kids, my kids and that's how some of you learn the most things about tech. They get on YouTube, they learn a video and they apply it. They don't go to school to learn how to do something. So there's a lot of what I call conversion rich content that's either available online like YouTube or somewhere else or here on theCUBE or here at Stanford that's being shared around in the communities that people can start on and then they apply it. So the key about this new community dynamic is when you have group interaction, it's iterating and there's practical ways to take something initially, share with a friend or collaborate quickly and master a skill much faster than the old ways of going to a classroom. We're going through a catalog where I learned it then you look to apply it. Here you're learning and applying in real time. This to me is a new dynamic that no one has yet cracked the code on and I think this is where the machine learning, this AI augmentation can really hit the sweet spot because you can combine social interaction with learning and the application of whatever that is, whether it's from play to something professional is pretty amazing. And bringing up that is a great point. We have several people that are educators that are professors that are going to be on the show today. We'd love to understand what you just said in terms of that real time learning and the ability to get something, learn it quickly and apply it. How that's evolving education of the next generation of computer scientists that are eventually going to be shaping our experiences. Stanford really pioneered the online. They had free online classes going back to years and they had unlimited, they broke all the records of free online classes on computer science. But the thing here that I'm looking at this event, I think it's interesting that we're in the middle of it happening in real time is that it's a movement, right? And this idea that digital women in data science here at Stanford, which you said what, a couple hundred people and they're 400 people and it has offices all around the world where there's more hundreds of people there. You add it up and it's in the tens of thousands of women around the world all connected around a live event right now. So the question is, what do they do next? How do you take that energy, that network that's been flash mobbed, if you will? And how do you turn that into value for either more learning, career advancement, research, applying it to society? To me, that is the next thing that I think that, you know, Judy Logan and her team will have to figure out and what we're going to try to help with is, how do you harness it? I mean, you put a Facebook group together, LinkedIn group, there's really no answer, right? So the only answer is to figure out, keep them together and keep the content flowing. That's my opinion. And I think that they're there because I think in the second annual and having somebody as powerful as Diane Greene as a keynote is really an indicator. There's tremendous momentum here, there's tremendous passion and the opportunity is limitless. So the next step is to your point, how do we figure out how to harness this in dry value globally? And not be too corporate shilling. Like one thing about these events that's great, it's authentic, real. When you start to get in the big dogs like Diane Greene and it begs the question, where's Amazon Web Services? Where's Microsoft? Satya Nutella? So, you know, you start to ask the question and then does it become a PR thing for them or do they stay authentic? So to me, I think if they stay on the authentic side, it'll work. But again, Diane Greene and what Google's doing is again, center. And Google, Google is like a mini Stanford too. People don't know that. So like having Google folks here really blends well with the Stanford culture and this culture here. So, yeah. What are some of the things that you're excited about looking at your experience here the first year? What are some of the things that you've seen change and what are you expecting to hear or hoping to hear from some of the great speakers that are here today? What I like about this conference is it's not just the women's conference, this is a tech conference. And I think what last year got me really pumped about it was, it was nothing to do with gender really in my mind. It was all about machine learning and AI stuff. There was a backdrop of all women here which is a phenomenal see. But it really is conversations around tech. And there was a kind of the impact of that content was there was a community fabric being developed. The hallway conversations last year were, hey, what are you working on? What projects are you working on? Or what company are you working for? So there was a solidarity around the bonding of we're all women in data science but it still was grounded in hardcore, big data, hardcore cloud computing, hardcore AI and machine learning. And as Diane Greene said, machine learning is at the heart of this and it blooms out into every vertical and she mentioned financial services to healthcare. But really, AI is going to be democratized and I think you're going to see that be an opportunity for these movements to continue. So that gets me the most excited. And the other thing is that seeing an event that's so awesome grow with a community fabric was the key. Fantastic. Well, looking forward to that and you talked about kind of the cross industry. We've got a great lineup of guests that are going to be on the program today from retail to leveraging big data and data science as a change agent for the way that jobs are recruited to machine learning and healthcare to educators. So stick around. This is Lisa Martin with John Furrier on theCUBE from Women in Data Science. The second annual will be right back.