Apply data science and statistics to generate insights tackling our COVID-19 challenge

We are a Stanford-hosted competition bringing together students from diverse backgrounds to apply big data analytics to challenges in healthcare. Undergraduate and graduate students with interests in Healthcare, Computer Science, Economics, Mathematics, Business, or other related fields are encouraged to participate. Participants will be provided with a few cases and corresponding data sets covering the broad range of health/biotech data studied by industry and academia to address specific problems and garner meaningful insights. Participants will use deep learning and statistical models/packages to explore the data provided, and use the insights to make recommendations and qualitative models for adoption by appropriate stakeholders.

Note: You must apply by 10/21/2020 @ to be eligible.

View full rules


$2,250 in prizes

Third Place

Second Place

First Place

Devpost Achievements

Submitting to this hackathon could earn you:


  • Participants: Undergraduate or graduate students at American universities; open to international students
  • Technology Needs: access to stable internet for Zoom conferencing


Teams will be required to submit their case by 11 AM PDT Sunday, 10/25. These deliverables should summarize your analysis methods and final conclusions. Judges will select top prizes based on these deliverables. Some important things to consider in your submission include:

  • Describe the methods used
  • Interpret results, concentrating on what you learned through the Datathon
  • Emphasize challenges in carrying out the analysis
  • Illustrate the originality and novelty of your approach
  • Reference any external sources you used to help you complete the task

Please submit 1) a written portion (can be either slide-deck or paper) and 2) a link to your code.


Faculty in Data Science, Statistics, Bioengineering, and Medicine
Stanford University

Judging Criteria

  • Originality and novelty of the approach
  • Quality of the description of the data and tools used, especially reproducibility
  • Soundness of approach taken
    ex. statistical significance, auROC
  • Potential scientific, societal, and policy impacts of the results
  • Quality of the 2-page write-up and execution of presentation