Press Release
January 4, 2023
Breaking Down Barriers in Data Science:
WiDS Launches 6th Annual Datathon to Make the Field More Global and Inclusive
WiDS Launches 6th Annual Datathon to Make the Field More Global and Inclusive
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The 6th Annual Stanford University Women in Data Science (WiDS) Datathon launches on January 4, 2023, bringing together people from across the globe to use data science to address real-world problems. In this year’s datathon challenge, participants use machine learning, artificial intelligence, and other data science methods to improve our ability to predict and prepare for extreme weather events caused by climate change, using a dataset created in collaboration with Climate Change AI (CCAI). The datathon will run until the end of February, with winners announced at the WiDS Stanford conference on March 8.
Last year, a record number of over 4,000 people registered for the WiDS Datathon, producing more than 25,000 solution submissions. Participants came from over 95 countries on 6 continents. Half of participants identified as beginners in data science. The WiDS Datathon is hosted on Kaggle, one of the world’s leading data science platforms. “Data science is booming. Women are still severely underrepresented in the field. Activities like the WiDS Datathon can help lower the barriers to entry and bring new and diverse people into the space. They also create community and connection,” says Dr. Margot Gerritsen, the Co-founder and Executive Director of WiDS and Professor Emerita at Stanford. The datathon is a key part of WiDS’ mission to elevate women in data science by providing them with inspiration, education, community, and support. What started as a one-day technical conference at Stanford University in 2015 has evolved into a global movement that includes yearly online and in-person conferences, a podcast series, skill-building workshops, an outreach program for secondary school students, and the annual WiDS Datathon. The datathon is organized by the WiDS Worldwide team at Stanford University, the WiDS Datathon Committee, Harvard University IACS, and Arthur.
Datathons have traditionally been male-dominated spaces. The WiDS Datathon is designed to remove barriers and attract people who might not otherwise participate in such an event. To create an environment where women feel comfortable contributing, WiDS requires that women make up at least half of each team. To encourage global participation, datasets are structured so that they can be easily manipulated on a laptop and with a low-bandwidth connection. WiDS ambassadors host dozens of workshops across the world to introduce people to the datathon, provide mentorship and skills training, and help individual participants link up with potential teammates. To attract and engage participants, WiDS Datathons focus on globally relevant problems with real-world impact. This year’s challenge gives participants the opportunity to help address extreme weather events caused by climate change. Participants from across the globe are already witnessing how heat waves, wildfires, droughts, and hurricanes are affecting their communities. “Data science and artificial intelligence have a critical role to play in fighting climate change. With the stakes this high, we need everyone at the table. Events like the WiDS Datathon help bring fresh perspectives and talent into the field of data science,” says Dr. Priya Donti, the Co-founder and Executive Director of Climate Change AI, a key partner in the 2023 WiDS Datathon. Accurate longer-term forecasts are crucial to help people predict and prepare for extreme weather events, which are becoming increasingly common. The physics-based models that currently dominate meteorology cannot see very far into the future. But by blending physics with machine learning, we can improve sub-seasonal forecasts that predict weather trends up to six weeks in advance. These models could help residents, governments, and aid groups anticipate and prepare for extreme weather, including making decisions about whether and when to evacuate.
The WiDS Datathon offers participants an opportunity to learn and refine their data science skills, collaborate and build networks with other data scientists, and contribute to the social good. The majority of past participants said the WiDS Datathon helped them gain new skills and connections and boosted their confidence in their data science abilities. Pravallika Myneni, a computer scientist from India who participated in the 2022 WiDS Datathon, had this to say about her experience: “The WiDS Datathon offered me a perfect platform to learn, apply and improve my data science skills, find a wonderful teammate and network with some amazing women in data science.” The WiDS Datathon committee believes in the power of data science to solve critical problems and drive innovation. Anyone interested in participating in this year’s datathon is invited to register here and spread the word to others who may want to join!
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Press Contacts
Judy Logan judy.logan@stanford.edu Sponsors WiDS sponsors Social Media Twitter: @WiDS_Worldwide LinkedIn: Women in Data Science (WiDS) Facebook: @WiDSWorldwide Videos WiDS YouTube Channel theCUBE 2022 WiDS Stanford Conference Datathon Worldwide News |
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About WiDS
Women in Data Science (WiDS) is an organization that seeks to elevate women in the field of data science, by inspiring, educating, and supporting female data scientists. What started as a single conference at Stanford University in 2015 has expanded to a global organization with participation across six continents, and a suite of programs that feature, connect and highlight outstanding women, doing outstanding work.
About WiDS
Women in Data Science (WiDS) is an organization that seeks to elevate women in the field of data science, by inspiring, educating, and supporting female data scientists. What started as a single conference at Stanford University in 2015 has expanded to a global organization with participation across six continents, and a suite of programs that feature, connect and highlight outstanding women, doing outstanding work.