Student Data Challenge
Code of Conduct
Each MinneMUDAC participant, mentor, judge, and organizer agrees to these guidelines before the challenge starts.
1. This work is for an actual client. You do not work for a leaderboard.
The 2026 MinneMUDAC industry partner is Food Group – a hunger-relief nonprofit. The Food Group shared their data for a reason. They want better answers to questions about how food reaches people in this community. The Food Group is your client for this event.
Two rules follow from this.
- Explainability is more important than technical correctness. Explainability is the property of a model that a person can follow and question. A model that your client understands is worth more than a model with a better score and no explanation. Assume that a judge asks you one question: “Why does the model say that?” Build your work so that you can answer it.
- Give recommendations that the client can use. Consider the staff, the budget, and the systems of the organization. An insight that needs resources they do not have is a finding. It is not a recommendation. Tell the judges which one you give them.
2. Compete hard. Treat people well.
Competition is good. It improves the work. It is part of the value of this event.
MinneMUDAC is run by volunteers. This includes the hosts, the judges, the mentors, and the staff of the industry sponsor. They left their jobs and their families for the day to run this event for you.
3. Data problems
Operational data is data that an organization collects during its daily work. Oddities, errors, and issues occur naturally in operational data. If something warrants discussion, report it to the event organizers. Assume positive intent rather than incompetence, negligence, or malice when these types of issues arise in data.
4. Cite your sources
Attribution is not just a format requirement. In professional work, it is important to give organizations credit for the use of their data as this is how public research stays funded.
Feeding America publishes datasets and methods at its own cost. The USDA Economic Research Service, the Bureau of Labor Statistics, and Minnesota DEED do the same. Funders see this work when other people credit it. Credited work is more likely to get renewed funding.
5. Use of the data
- Use the data for this challenge only.
- Do not redistribute the data.
- Do not post the data in public.
- Do not load the data into an AI service that keeps your input for model training.
- Delete your copies of the data after the event. The organizers will tell you if a different rule applies.
- Stop work on a record that contains personal information. Report that record to an organizer.
Acknowledgment
You agree to this Code of Conduct when you participate. You confirm that you read it.
The Food Group can receive your work. The Food Group can use your work in their operations. Any use of your work will name your team.