World's First and Only MLH Data Science Hackathon!

Check out our website for everything you need to know: tamudatathon.com

A datathon is where you build your analytics skill set and create data driven solutions. We provide data science lectures, workshops, challenges, prizes, fun activities, swag, and more. We’ll take care of you while you learn & create!

View full rules

Prizes

$10,385 in prizes

1st Place TD Make-Your-Own Challenge

1st Place $400 x 4 team members

2nd Place TD Make-Your-Own Challenge

2nd Place $200 x 4 team members

3rd Place TD Make-Your-Own Challenge

3rd Place $100 x 4 team members

TD Stock Prediction Challenge

$500 x 4 team members

TD For-You Page Challenge

$400 x 4 team members

TD City Search Tool Challenge

$300 x 4 team members

MLH Best Domain Registered with Domain.com

PowerSquare Qi Wireless Phone Chargers & Domain.com Backpacks

MLH Best use of Google Cloud

Google Home Minis

1st Place Walmart Challenge

1st Place - Dell Monitors

2nd Place Walmart Challenge

2nd Place - Bose Speakers

3rd Place Walmart Challenge

3rd Place - Instax Instant Cameras

Capital One Challenge

$250 x 4 team members

Mathworks Challenge

$125 x 4 team members

HPE Challenge (2)

Raspberry Pi 4 Starter Kits

Devpost Achievements

Submitting to this hackathon could earn you:

Eligibility

TAMU Datathon is open to any enrolled undergraduate or graduate student who is at least 18 years of age, as well as people who have graduated within 1 year from the event. We welcome students from all across the world and from all majors!

Judges

James Caverlee

James Caverlee
Professor of Computer Science and Engineering

Darren Homrighausen

Darren Homrighausen
Associate Professor of Statistics

Dylan Shell

Dylan Shell
Professor of Computer Science and Engineering

Yang Shen

Yang Shen
Assistant Professor, Electrical & Computer Engineering

Eric Zavesky

Eric Zavesky
AT&T

 Shreya Punya

Shreya Punya
Facebook

Brandon Walker

Brandon Walker
IBM

Humza Jaffri

Humza Jaffri
TAMU Hack

Judging Criteria

  • Purpose
    Communicated a clear understanding of the problem
  • Framework
    Mapped the task to a Data Science problem
  • Data Use
    Effectively used data, acquired additional data
  • Models & Analytics
    Effective application of analytics
  • Validation
    Assessed quality of solutions & models
  • Impact
    Clear description of the impact the solution has on solving the problem
  • Presentation
    Effectiveness, Engagement and Team Performance

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