Entering the Next Phase in Data Science with Sumana Ravikrishnan
Written by Jacob Yoss
Sumana Ravikrishnan is a Data Scientist at ServiceNow, an enterprise software company, a Director of WWCode Silicon Valley, and a WWCode Data Science Track lead, an online community dedicated to the field. She recently sat down with our Content Creator, Jacob Yoss, to discuss data science’s recent growth, ethics, and educational leadership.
How did you get involved with Women Who Code, and how long have you been leading the Data Science Track?
I’ve been involved with Women Who Code for five years now. After grad school, I moved to California for work, and around that time I came across the local chapter of Women Who Code. I started volunteering for events led/organized by my local Silicon Valley chapter and eventually immersed myself in organizing introductory boot camps in data analysis and leading research study groups. When the organization announced its six global Track communities — one of them focusing on data science — the timing was perfect because I really enjoyed putting together educational content for my local chapter and was excited at the opportunity to have an impact on a wider audience!
What do you love about the data science field?
I love it because everyone is talking about it and wants to be a part of it. Data science comprises so many facets that anyone interested can get involved, regardless of whether they know high-level math or how to implement an ML algorithm from the get-go. It’s an advantage to have a statistical background to excel in the field, but it’s not entirely necessary.
However, the fact that everyone wants in on data science is also a source of apprehension. Because anyone can pick it up, it’s becoming a black box that people don’t understand as thoroughly as they should. This pattern makes me more passionate about educating people about the foundations before they dive in too quickly and tackle something they’re not ready for. That’s what I try to incorporate into my Track courses.
That’s awesome. What direction do you want to take the track in, and how are you already working toward that?
I’ve been with the Data Science track from its early days. The first fellow was Madeleine Shang, who had put together coding boot camps that catered to beginners. She needed someone to help teach those courses, so I volunteered. I noticed a gap between our existing educational content and the need for building the content around the foundational concepts, which motivated me to write curricula for new courses. I put together three extensive boot camps last year across beginners to advanced learners, and a few panels featuring inspiring women making waves in the field.
I’m not alone, though. It’s thrilling to have other volunteers kickstarting more webinars. One recent series taught statistics, which is an important foundational block for the field of Data Science. Ultimately, that’s the direction I’d love to see the track go in — providing content for people without a computer science or math background, but sufficiently preparing them to apply data science principles to whatever projects they’re working on.
At what point did you realize that you wanted to lead the track instead of just participate?
After a couple of successful series, I was actively participating in the community and helping out other track members with anything they needed, like help understanding new concepts, or tips on putting a webinar together. I really enjoyed taking on more responsibilities, so I figured I should also take on a leadership role to enable more people to approach me directly. Observing how my relationship with education changed over time, I am driven to lead that change for others as well!
A personal story that I think of is when I first learned calculus. I took classes through high school and college, but I had no idea what I was doing until grad school. I spent six years of my life implementing calculus without any clue about what it was used for! Data science is a black box for many, and calculus was mine.
Understanding how the concepts you learn relate to the real world was eye-opening for me, and I want others to experience the same thing. Making that happen for people is one of my biggest motivators. As different fields blow up, it is natural for people to flock to them — for example, everyone wants to take advantage of machine learning, but not everyone knows how to use it. I feel it’s imperative that they actually understand the theoretical aspects of these fields and don’t just jump on the bandwagon for the sake of it. My goal is to bridge the gap between interest and experience.
Your digital events are very successful; how do you go about attracting attendees and making them go smoothly?
The first webinar series I created had over 500 attendees, which is when I realized that countless people are genuinely interested in learning data science (that’s one of the events that motivated us to increase our Zoom audience limit from 100 to 500!). For all events, our team promotes them on most social media platforms. I started the Data Science track’s Instagram account years ago, so I try to be as active as possible and push much of our content. At least 100 people engage with each of the account’s posts and stories. Even if only 10% of the audience attends a digital event, that’s ten people coming through one social media platform, which makes the effort worthwhile.
As for executing events, I learned many lessons from the first series I organized and created a kind of template that I’ve used for subsequent webinars. I’ve refined it over time, adjusting with trial and error. Other track organizers now use a similar format (including for our big statistics series this season, which is available on YouTube). We have a checklist we go through so that webinars (hopefully) go off without a hitch. It’s rather crude — it’s mostly scattered across different Excel spreadsheets — but it’s a starting point that serves us well!
Do you have any prior teaching experience, or is this your first time educating people in this capacity?
If coaching friends before exams counts as “teaching experience,” then yes, I do! Other than that, this is my first time teaching, but I do come from a family of educators, so maybe it is in my DNA! Jokes aside, I really enjoy it, and I’m grateful to Women Who Code for giving me the chance to experience this side of the industry.
It sounds like you have a knack for it! What do you say makes a good leader?
Communication skills are essential. Even if I don’t have the answer to someone’s question, acknowledging that and pointing them in the right direction sets them up for success and establishes trust. Communication also applies to touching base with people and letting them know you care about their journeys, which is more important than ever in our current digital landscape.
How else do you foresee data science growing, and why do you believe it’s imperative people learn more about the why than just the how?
Although data science is growing fast, I don’t believe the field’s ethical side is keeping up. Answering the ethical questions around this discipline is an evolving process, but I feel artificial intelligence is booming at such an exponential rate that we aren’t pausing to think: “I can, but should I?”
The next big step is to define and standardize rules regarding how data can be handled. People are rightfully increasingly angry and suspicious about how companies use their data for advertising. We need rules and guidelines that prevent data abuse, so that’s where I predict the field will head toward next.
To learn more about practical and ethical data science, sign up for WWCode’s Data Science Track or check out our Events Page.