The first MLH Data Science Hackathon in the world!

For the first time, TAMU Datathon is banding together data enthusiasts from across multiple disciplines, skill levels, and universities. TAMU Datathon is a 24-hour event where companies, researchers, and hundreds of students immerse themselves in data science.

We’re coming together to solve real-world challenges, learn about data science, hear from leaders in research, and compete for company and university prizes.

This all happens in a fun, interactive, and competitive way by providing our participants the option to take part in the Learner Track or the Competitor Track. We believe the TAMU Datathon will inspire students across all disciplines to integrate quality data-driven practices into their field of expertise.

View full rules

Prizes

$13,560 in prizes

Facebook

- 1st Place: Oculus Quest All-in-one VR Gaming Headset (64GB)

(Plus, an opportunity of working towards the next “State of the Map” Conference)

ConocoPhillips (3)

- Oculus Quest All-in-one VR Gaming Headset
- Powerbeats Pro 3
- JBL Flip 5

(1st place gets first pick of prizes, 2nd gets next pick, 3rd gets remaining prize)

CBRE

- 1st Place: Amazon Echo

Goldman Sachs

1st Place: Amazon Echo Show

Shell

- 1st Place: Shell Goodie Bags & 50$ Shell gift-card

Walmart Technology

- 1st Place: Bose headsets
- 2nd Place: Go Pros
- 3rd Place: Instant cameras

Best use of Google Cloud

Google Home Minis for each team member

Best Domain Name from Domain.com

Domain.com branded backpack for each team member.

Best Automation Hack with UiPath

Build an automation hack using UiPath! Each winning team member will receive a UiPath/MLH Branded Backpack and DROCON Drone!

Devpost Achievements

Submitting to this hackathon could earn you:

Eligibility

  • Participants: 
    • Applicants that have RSVP'd to the event invitation
    • Teams or Individuals
    • Any major from any university
    • Freshman to Ph.D. students
  • Countries: 
    • Host country - United States
    • Available to any country
 

Judges

Nick Duffield

Nick Duffield
duffieldng@tamu.edu

Dilma Da Silva

Dilma Da Silva
dilma@cse.tamu.edu

Edward Jones

Edward Jones
ejones@stat.tamu.edu

Bani Mallick

Bani Mallick
bmallick@stat.tamu.edu

Darren Homrighausen

Darren Homrighausen
darrenho@tamu.edu

Alan Dabney

Alan Dabney
adabney@stat.tamu.edu

Tanzir Ahmed

Tanzir Ahmed
tanzir@tamu.edu

Yang Shen

Yang Shen
yshen@tamu.edu

Nagaraj Thenkarai Janakiraman

Nagaraj Thenkarai Janakiraman
tjnagaraj@tamu.edu

Chuan Yuan Hsu

Chuan Yuan Hsu
chsu1@tamu.edu

Grace Yoon

Grace Yoon
gyoon@stat.tamu.edu

Raniero Lara-Garduno

Raniero Lara-Garduno
raniero@tamu.edu

Dezhen Song

Dezhen Song
dzsong@cse.tamu.edu

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
  • Oral Presentation
    Effectiveness, Engagement and Team Performance

theme

  • Machine Learning/ AI