About the Data Science Bowl
The Data Science Bowl, presented by Booz Allen and Kaggle, is the world’s premier data science for social good competition. No single person or organization can meaningfully tackle problems of immense magnitude, and no one should have to fight alone.
“We created the Data Science Bowl in 2014 in order to convene communities to take on huge challenges, and give data scientists the opportunity to contribute to something bigger than themselves.”
—Booz Allen Senior Vice President Josh Sullivan & Principal Steve Mills.
The Data Science Bowl brings together data scientists, technologists, domain experts, and organizations to take on the world’s challenges with data and technology. It’s a platform through which individuals can harness their passion, unleash their curiosity, and amplify their impact to effect change on a global scale.
To present the competition, Booz Allen partnered with Kaggle, the leading online data science competition community with over 760,000 members around the world. During a 90-day period, participants, either alone or working in teams, gain access to unique data sets to develop algorithms that address a specific challenge. And each year, the competition awards hundreds of thousands of dollars in prize money to top teams.
In 2014 – 2015, participants examined more than 100,000 underwater images, provided by the Hatfield Marine Science Center, to assess ocean health at a massive speed and scale. More than 1,000 teams participated, submitting more than 15,000 solutions to the challenge. The winning team, Team Deep Sea, developed a classification algorithm that beat the current state of the art by more than 10%.
In 2015 – 2016, they applied analytics in cardiology, transforming the practice of assessing heart function. Though the challenge was decidedly more complex than the prior year, this competition received nearly 1,400 submissions from more than 700 teams. In fact, the winning team, Tencia Lee and Qi Liu, are hedge fund traders, not traditional data scientists.
In 2016 – 2017, competitors used anonymized, high-resolution lung scans from hundreds of patients provided by the National Cancer Institute (NCI), to create algorithms that can improve lung cancer screening technology. The participants created algorithms that can accurately determine when lesions in the lungs are cancerous and thereby dramatically decreasing the false positive rate of current low-dose CT technology.
Submit Your Ideas
Help us build an unprecedented future.
We are searching for the next Data Science Bowl challenge—a problem with the potential to change the world. If selected, the power of the entire data science community will be harnessed against it.