About the Rowdy Datathon
The Rowdy Datathon is a free, weekend-long, overnight hackathon focused on data science where students can network, learn about data science, code, and collaborate!

Team up with other students to tackle a data science challenge for a chance to win prizes! Mentors will be available to help you work through the challenge! Attend workshops, mini events, socials, and network with our industry partners!

Attendance is FREE and all meals, snacks, and drinks are provided. We welcome students from all disciplines, backgrounds, and technical levels! 

We’re excited to announce you’ll be fluttering with some new data this year!
We are embracing our Texan roots to give you a wonderfully themed weekend where all our participants, Hackers, can provide insights and advice on critical challenges!

We’ll give you everything you need
No need to prepare your own dataset or project idea. We will give all participants the same dataset to use for our challenge.

No Experience Necessary
To help you navigate the challenge, learn data science skills, and make the most of the weekend, we will provide mentors and workshops. We will also have tracks for beginner, intermediate, and advanced data scientists. The focus of this event is to foster your growth and excitement for data science.

Fly with a team or coast solo
We will have a team-building mini event to help you find your dream team. If you choose to team up beforehand, remember that the challenge will require mathematics, statistics, data ethics, AND computer science knowledge, so go multi-disciplinary if you can!

• Compelling Presentation - Informative
  Did the team create meaningful tables and plots to present data? Evaluate effectiveness of graphics and assess audience needs. Create accurate, meaningful visuals that align with presentation goals. Critically assess graphics for accuracy.
• Compelling Presentation - Understandable
  Did the team prioritize information over data? Weigh the merit and impact of solutions, converting data into actionable insights. Present arguments and outcomes clearly and coherently.
• Analysis Comprehension
  Did the team describe the analysis plan and data exploration? Conduct exploratory analysis, apply methods and tools, and evaluate results. Critically assess datasets for errors and compare results with other findings.
• Sound Technical Methods
  Did the hackers demonstrate knowledge of data analysis tools and techniques? Select and apply appropriate tools and techniques for data analysis.
• Awareness of the Data Context
  Is the team aware of legal and ethical issues with data? Apply and work with data ethically. Understand and explain data biases.
• Reproducible Results
  Did the hackers demonstrate knowledge of data uses? Support an environment for critical data use. Was the process scripted with version control? Ensure reproducibility with minimal manual steps.