WiDS Datathon Committee members, Srujana Kaddevarmuth and Susan Malaika, wrote this article about the WiDS Datathon. Srujana is Director, Data Science & Value Realization, Walmart Labs and WiDS Bengaluru ambassador. Susan Malaika is Senior Technical Staff, IBM. Both Walmart Labs and IBM sponsor the Global Women in Data Science (WiDS) initiative.
Participate! Deadline to enter: February 24
Time is running out! February 21st is the team merger deadline, and February 24th is the last day to enter the competition and submit your entries for the Women in Data Science (WiDS) Datathon 2020. This year the contest is focused on patient health, with data from MIT’s GOSSIS (Global Open Source Severity of Illness Score) initiative.
The WiDS Datathon is hosted on Kaggle, an online community of data scientists and machine learners. The winners will be determined by the leaderboard on the Kaggle platform at the time the Datathon closes on February 24. Winners will be announced March 2 at the WiDS Conference at Stanford University and via livestream.
The Datathon is open to individuals or teams of up to 4. At least half of each team must be women (individuals identifying as female participants). As of Feb 17, there were 522 teams competing from approximately 85 countries.
Why should I participate?
Feedback we received from participants at a recent datathon on-boarding session in Bengalaru includes:
“Before coming to the WiDS 2020 Bengaluru datathon workshop, I was not sure if I would be able to participate in a Kaggle competition. After meeting and hearing the women at the workshop, I felt motivated to try my hand at it. All the speakers and mentors were so encouraging that by the end of the day, I registered for the competition and got started with the Datathon. I suppose the workshop was the “Nudge” I needed to get started. Thank You! WiDS Bengaluru ambassadors and organizers.” — Preeti Ravikiran
And here is feedback we received from WiDS datathon participants in prior years:
“Just do it. Don’t be afraid to explore and try it out. It’s a great learning experience.”
“I think you can gain something from the exercise no matter what level of capability, or time commitment you have to give. The learning will likely be commensurate with the amount of effort you are able to give to the task, but even if you have very little time, or lack of experience, you can still get something.”