WiDS Puget Sound Datathon 2026
WiDS Puget Sound Datathon 2026
📅 April 8, 2026 | 📍 Seattle
About the Event
Join WiDS Puget Sound for an exciting datathon exploring how data can help solve a critical social impact challenge - predicting wildfire impact! This event is inspired by and aligned with the WiDS Worldwide Datathon, hosted on Kaggle. We will be working with the same dataset and problem statement as the global competition.
This is a fantastic opportunity to apply your data science skills, collaborate with peers, and contribute to meaningful, real-world problem solving.
For more details on the global competition, visit: https://www.widsworldwide.org/learn/datathon/
What is the difference between the local and global competitions?
The WiDS Puget Sound event is designed as a “companion event”. We encourage all local participants to also enter the WiDS Worldwide Kaggle competition.
SUBMISSION FORMAT: Our WiDS PS Datathon Contest will be judged based on each team’s 10-min, 1-slide project presentation.
TIMELINE: The WiDS PS Contest is due earlier! Our slide submission deadline (April 5) and presentation day (April 8) were chosen to give you the opportunity to incorporate feedback from our judges into your work for the global competition. If desired, you may meet other teams at our presentation event and decide to collaborate on an entry for the global competition.
PRIZES: The top presenters will be invited to deliver a lightning talk on their project at the upcoming WiDS Puget Sound conference. There are no cash prizes for the WiDS PS event.
ELIGIBILITY: For the WiDS PS Contest, participants must be located in the greater Seattle area, and able to attend the in-person final presentation on April 8 in Seattle.
Please register as a participant to receive final location details.
Key Dates
Registration open in March
Slide deck due: April 5, 2026
Final presentation: April 8, 2026 ,Seattle
The WiDS Worldwide Datathon deadline is May 1, 2026. Team merges are permitted before April 24.
Submission & Presentations
Submit a 1-slide ‘poster’ by April 5, 2026.
The poster should summarize your approach to solving the datathon challenge
Some examples of relevant topics include:
Exploratory Data Analysis - The steps you took to examine the dataset, and what relevant information you learned.
Data Cleaning & Feature Engineering - Outline the process of preparing your data for modeling.
ML Architecture - What types of model did you experiment with? What factors influenced your choice?
Hyperparameter Optimization - How did you determine what values to use for hyperparameters?
Cross-validation - Describe your steps for evaluating & comparing model performance.
Challenges encountered - Did you experience difficulties at any of the steps above?
Deliver a 10-minute in person presentation on April 8.
To enter the WiDS Worldwide competition, you must submit your final solution and code repository/link via Kaggle.
Eligibility
Open to all skill levels - beginners and experienced practitioners welcome!
Participants must be local and able to attend the in-person final presentation on April 8 in Seattle
A Kaggle account is required to access the dataset
Compete individually or in a team of 2 - form your own team before registering
As always, our events are open to all genders.
If you aren’t local to the greater Seattle area, you should still check out the global competition linked above!
Prize
🏆 The top presenters will be invited to present their work at the WiDS Puget Sound Annual Conference on May 8, a great opportunity to showcase your project to data science professionals and enthusiasts in the community!
Support & Office Hours
You won't be on your own! We'll be hosting weekly office hours throughout the datathon to provide mentorship and guidance as you work toward your final presentation. Details on scheduling will be shared after registration.
Frequently Asked Questions: https://www.widspugetsound.org/datathon-2026#faq
More questions?
Email us at [email protected]
WiDS Puget Sound is independently organized by Diversity in Data Science to be part of the mission to increase participation of women in data science and to feature outstanding women doing outstanding work.