 Live from Stanford University, it's The Cube, covering Global Women in Data Science Conference, brought to you by SiliconANGLE Media. Good morning and welcome to The Cube. I'm Lisa Martin and we are live at the Global Fourth Annual Women in Data Science Conference at the Ariyaga Alumni Center at Stanford. I'm very pleased to be joined by one of the Woods Ambassadors this year, Srejna Katavarmath, Data Science Senior Manager at Google, and as I mentioned, you are an ambassador for Woods in Bangalore. The event is Saturday, Srejna, welcome to The Cube. Thank you, pleasure is mine. So this is the fourth annual Women in Data Science Conference this year, over 150 regional events of which you are hosting Bangalore on Saturday, March 9th. 50 plus countries, they're expecting 100,000 people to engage. Tell us a little bit about how you got to be involved in Woods. Yeah, so I care about data science, but also about accurate representation of women and gender minority in this space. And I think with Global Initiative is doing amazing job in creating a significant impact globally. And that kind of excited me to get involved with Woods Initiative. So you have, which I can't believe, you are an SME with 10 plus years experience in data analytics focusing on marketing and customer analytics. You've had senior analytics leadership positions at Accenture, Hewlett Packard, now Google. Tell me a little bit about, before we get into some of the things that you're doing, specifically the Datathon, your experience as a female in technology the last 10 plus years. Yeah, it's been exciting. I started my career as an engineer. I wanted to be a doctor. Fortunately, unfortunately it couldn't happen and I ended up being an engineer. And it has been an exciting ride since then. I felt that I had a passion for doing pursuing management. And I pursued management and specialization with operational research and project management. And I started my career as a data scientist, worked my way up to different leadership positions and currently I'm leading a portfolio for Accenture at Google. Yeah, in the data science domain. Yeah, it's exciting. Absolutely. So one of the things that is happening this year at Woods 2019 is a second annual Datathon. That's right. Really looking at a predictive analytics challenge for social impact. Tell us a little bit about why Woods is doing this Datathon and what you're doing in that respectively in Bangalore. Okay, so you see data science in itself is a highly interdisciplinary domain and it requires people from different disciplines to come together. Look at the problem from different perspectives to be able to come up with the most amicable and optimal solution at any given point of time. And Datathon is one such avenue that fosters this collaboration. And Datathon is also an interesting avenue because it helps young data science enthusiasts hone the required data science skill sets and also helps the data science practitioners enhance and sustain their skill sets. And that's the reason Woods Bangalore was keen on supporting Woods Global Datathon initiative. So the skill sets, I'd like to kind of dig into that a bit because we're very familiar with those required data analytics skill sets from a subject matter expertise perspective. But there's other skill sets that we talk about more and more with respect to data science and analytics and that's empathy, it's communication, negotiation. Can you talk to us a little bit about how some of those other skills help these Datathon participants, not just in the actual event but to further their careers? Absolutely, so when you enter the real world, so there are a lot of these challenges wherein you would require a domain expert, you would require someone who has a coding experience, someone who has experience to handle multiple data science programmatically and also you need someone who has a background of statistics and mathematics. So you would need different people to come together, look at the problem and then be able to solve the challenges, right? So collaboration is extremely pivotal, it's extremely important for us to put ourselves in other shoes and see, look at the problem and look at the problem from different perspective and collaboration are the key to be able to be successful in data science domain as such. Okay, so let's get into the specifics about this year's dataset and the teams that were involved in the Datathon. All right, so this year's Datathon was focused on using satellite imagery to analyze the scenario of deforestation, cost-oil pump plantations. So what we did at, with Bangalore is we conducted a community workshop because our research indicated that men dominated the Kegel leaderboard, not just in Bangalore but for India in general, despite that region having amazing female data scientists who are innovators in their space with multiple patents, publications and innovations to their credit. So we asked few questions to certain female data scientists to understand what could be the potential reason for their lower participation on the Kegel as a platform and their responses led us to these three reasons. Firstly, they may not have the awareness about Kegel as a platform. Can you tell me a little bit more about that platform so our viewers can understand that? All right, so Kegel is a platform wherein a lot of these datasets have been posted if anybody is interested to hold required data science skills as they can definitely try, explore, build some codes and submit those codes and the teams that are submitting the codes which are very effective, having great accuracy, would get scored on the Kegel leaderboard and you know that which is the most effective solution that can be implemented in the real world. So we conducted this data sound workshop and one of the challenges that most of the female data scientists face is having an environment to network collaborate and come up with a team to be able to attempt a specific data sound challenge that is in hand. So we conducted a data sound workshop to help participants overcome this challenge and to encourage them to participate in its global data sound challenge. So what we did as a part of this workshop was we gave a demo on how to navigate Kegel as a platform and we connected an event specifically focused on networking so that participants could network from teams. We also conducted a deep, in-depth technical session focusing on deep neural nets and specifically on convolutional neural nets, the understanding of which was pivotal to be able to solve this year's data sound challenge and the most interesting part of this data sound workshop was the mentorship guidance. We are able to line up some amazing mentors and assign these mentors to the concerned or the interested participating teams and these mentors work with their respective teams through the next three weeks and provide them with the required guidance, coaching and mentorship and help them through their data sound journey. That's fantastic. So over a three-week period, how many participants did you have? There were around 110 plus people for the event. Yeah, for the event. And there were multiple teams that were formed and we assigned those mentors. We identified seven different mentors and assigned these mentors to the interested participating teams. We got a great response in terms of amazing turnout and the event, new teams got formed, new relationships got initiated. New relationships, new collaborations. All right, tell us about those achievements. So there were, there was one team from engineering branch or engineering division who were very new to the Kegelers platform. They had their engineering exams coming up but despite that, they learned a lot of these new concepts, they formed the team, they worked together as a team and were able to submit the code on the Kegel leaderboard. They were not the top-scoring team but this entire experience of being able to collaborate, look at the problem from different perspective and be able to submit the code despite a lot of these challenges and also navigate the platform in itself as a decent achievement from my perspective. A huge achievement. Yeah, huge achievement. So here you are at Stanford today. You're going to be flying back to go host the event there. Tell us about, from your perspective, if we look at the future line of sight for data science, let's just take a peek at the momentum that this WID's movement is generating. This is our fourth year covering, the fourth annual event, fourth year on theCUBE and we see tremendous, tremendous momentum with not just females participating and the WID's leaders providing this sustained education throughout the year, the podcast, for example, that they released a few months ago on Google Play and iTunes but also the number of participants worldwide. As you look at where we are today, what in your perspective is the future for data science? All right, so data science as a domain is evolving at a lightning speed and may possibly hold the solution to almost all the challenges faced by humanity in the near future, but to be able to come up with the most amicable and sustainable solution that's more relevant to the domain, achieving diversity in this field is a must and initiatives like WID's help achieve that diversity and foster a greater impact. Absolutely. Melissa Regina, thank you so much for joining me on theCUBE this morning, live from WID's 2019. We appreciate that. Wish you the best of luck at the WID's local event in Bangalore over the weekend. Thank you. It was a pleasure discussing with you. Likewise. Thank you. We want to thank you. You're watching theCUBE live from Stanford University at the fourth annual WID's conference. I'm Lisa Martin. Stick around. My next guest will join me in just a moment.