 Live from Stanford University, it's theCUBE. Covering Stanford Women in Data Science 2020. Brought to you by SiliconANGLE Media. Hi, and welcome to theCUBE. I'm your host, Sonia Tagare, and we're live at Stanford University covering the 5th Annual WIDS Women in Data Science Conference. Joining us today is Emily Glasberg Sands, the head of data science at Coursera. Emily, welcome to theCUBE. Thanks, so great to be on. So tell us a little bit more about what you do at Coursera. Yeah, absolutely. So Coursera is the world's largest platform for higher education. We partner with about 160 universities and 20 industry partners, and we provide top learning content from data science to child nutrition to about 50 million learners around the world. I lead the end-to-end data team, so spanning data engineering, data science, and machine learning. Wow, and we just had Daphne Kolaron earlier this morning who's the co-founder of Coursera, and she's also the one who hired you. So tell us more about that relationship. Well, I love Daphne. I think the world of her, as I will talk about shortly, she actually didn't hire me from the start. The first answer I got from Coursera was a no, that the company wasn't quite ready for someone who wasn't a full-blown coder, but I eventually talked her in to bring me on board, and she's been an inspiration ever since. I think one of my first memories of Daphne was when she was painting the vision of what's possible with online education, and she said, think about the first movie. The first movie was literally just filming a play on stage. I'll appreciate this, give her your background in film. And then fast forward to today and think about what's possible in movies that could never be possible on the brick and mortar stage. And the analog she was creating was that the first MOOC, the first massive open online course, was very simply filming a professor in a classroom, but she was thinking forward to today and tomorrow and five years from now, and what's possible in terms of how data and technology can transform how educators teach and how learners learn? That's very cool. So how has Coursera changed from when she started it to now? So it's evolved a lot. So I've been at Coursera about six years when I joined the company, had less than 50 people. Today we're 10 times that size, we have 500. I think there have been obviously dramatic growth in the platform overall, but three main changes to our business model. The first is we've moved from partnering exclusively with universities to recognizing that actually a lot of the most important education for folks in the labor market is being taught within companies. So Google is super incentivized to train people in Google Cloud, Amazon in AWS. Folks need to learn Tableau and a whole host of other softwares. So we've expanded to including education that's provided not just by top institutions like Stanford, but also by top institutions that are companies like Amazon and Google. The second big change is we've recognized that while for many learners, an individual course or a MOOC is sufficient, some learners need access to full degree diploma bearing credentials. So we've moved to the degree space. We now have 14 degrees live on the platform, masters in computer science and data science, but also in business accounting and so on. And the third major changes, I think just sort of as the world has evolved to recognize that folks need to be learning throughout their lives, there's also general consensus that it's not just on the individuals to learn, but also on their companies to train them and governments as well. And so we launched Coursera for Enterprise, which is about providing learning content through employers and through governments so we can reach a wider swath of individuals who might not be able to afford it themselves. And how are you able to use data science to track individual user preferences and user behavior? Yeah, that's a great question. So you can imagine, right, 50 million learners, they're from almost every country in the world, they're from a range of different backgrounds, have a bunch of different goals. And so I think what you're getting at is that so much of creating the right learning experience for each person is about personalizing that experience. And we personalized throughout the learner journey. So in discovery, up front, when you first join the platform, we ask you, what's your career goal? What role are you in today? And then we help you find the right content to close the gap. As you're moving through courses, we predict whether or not you need some additional support, whether it's a fully automated intervention, like a behavioral nudge, emphasizing growth mindset, or a pedagogical nudge, like recommending the right review material and provide it to you. And then we also do the same to accelerate support staff on campus. So we identify for each individual what type of human touch might they need and we serve up to support staff recommendations for who they should reach out to, whether it's a counselor reaching out to a degree student who hasn't logged in for a while, or a TA reaching out to a degree student who's struggling with an assignment. So data really powers all of that, understanding someone's goals, their backgrounds, the content that's going to close the gap, as well as understanding where they need additional support and what type of help we can provide. And how are you able to track this data? Are you using A-B testing? Yeah, great question. So the, we call it a venting level data, which basically tracks what every learner is doing as they're moving through the platform. And then we use A-B testing to understand the influence of kind of our big features. So say we roll out a new search ranking algorithm or a new learning experience. We would A-B test that, yes, to understand how learners in the new variant compare to learners in the old variant. But for many of our machine learning systems, we're actually doing more of a multi-arm banded approach where on the margin we're changing a little bit the experience people have to understand what effect that has on their downstream behavior separate from this mass hold in or hold out A-B test. And so today you're giving a talk about Coursera's latest data products. Give us a little insight about that. So I'm covering three data products that we've launched over the last couple of years. The first two are oriented around really helping learners be successful in the learning experience. So the first is predicting when learners are going to need additional nudges and intervening in fully automated ways to get them back on track. The second is about identifying learners who need human support and serving up really easily interpretable insights to support staff so they can reach out to the right learner with the right help. And then the third is a little bit different. It's about once learners are out in the labor market, how can they credibly signal what they know so that they can be rewarded for that learning on the job. And this is a product called skill scoring where we're actually measuring what skills each learner has up to what level. So I can, for example, compare that to the skills required in my target career or show it to my employer so I can be rewarded for what I know. And that can be really helpful when people are creating resumes by ranking how much of a skill that they have. Absolutely, so it's really interesting when you talk about resumes, so much of what's shown on resumes are traditional credentials. Things like what school did you go to? What did you major in? What jobs have you had? And as you and I both know, there's unequal access to the school you go to or the early jobs you get. And so part of the motivation behind skill scoring is to create more equitable or fair or accessible signals for the labor market. So we're really excited about that direction. And do you think companies are taking that into consideration when they're hiring people who say have like a five out of five skill in computer science, but they didn't go to Stanford? You think they're taking that? Absolutely, I think companies are hungry to find more diverse talent. And the biggest challenge is when you look at people from diverse backgrounds, it's hard to know who has what skills. And so skill scoring provides a really valuable input. We're actually seeing it in use already by many of our enterprise customers who are using it to identify who of their internal employees is well positioned for new opportunities or new roles. For example, I may have a bunch of back end engineers. If I know who's good in math and machine learning and statistics, I can actually tap those folks to transition over to machine learning roles. And so it's used both as an external signal and external labor market as well as an internal signal within companies. And just our last question here, what advice would you give to young women who are either out of college or just starting college who are interested in data science, who maybe don't have it majored in a typical data science major? What advice would you give to them? So I love that you asked who haven't majored in a typical data science major. I'm actually an economist by training. And I think that's probably the reason why I was at first rejected from Coursera because an economist is a very strange background to go into data science. I think my primary advice to those young women would be to really not get too lost in the data science, in the math, in the algorithms, and instead to remember that those are a means to an end and the end is impact. So think about the problems in the world that you care about. For me it's education, for others it's healthcare or personal finance or a range of other issues. And remember that data science provides this vast set of tools that you can use to solve the problems you care about most. That's great. Thank you so much for being on theCUBE. Thank you. I'm Sonia Tugare. Thank you so much for watching theCUBE and stay tuned for more.