DrivenData Competitions

Website: http://www.drivendata.org/

Overview

DrivenData Competitions is an online platform hosting data science and machine learning challenges focused on social impact. It connects mission-driven organizations with a global community of data scientists to solve real-world problems in areas such as public health, international development, education, conservation, and public services.

Key Details

• Category: Themed directories / data science & AI competitions
• Focus Areas: Social impact, public good, non-profit and mission-driven projects
• Typical Domains: International development, health, education, research and conservation, public services
• Scope: 80+ organizations and 150+ projects (as reported)

Features

• Social impact–focused competitions

  • Online machine learning and data science challenges centered on public good problems.
  • Problems sourced from real organizations with practical deployment potential.

• Curated real-world datasets

  • Datasets provided for each competition, often from partner organizations (e.g., city governments, NGOs, academic institutions, private-sector partners).

• End-to-end challenge lifecycle

  • Problem scoping with organizations to identify data-driven impact opportunities.
  • Competition design and hosting on the DrivenData platform.
  • Model submission, ranking, and evaluation.
  • Support for transitioning winning approaches into usable tools and workflows for partners.

• Open solutions and learning resources

  • Prize-winning solutions from past competitions are shared publicly on GitHub.
  • Open-source projects maintained for the data science community, including tools and reference implementations used in or derived from competitions.

• Collaboration with mission-driven organizations

  • Direct project work with nonprofits, foundations, governments, and other impact-focused organizations.
  • Example projects:
    • Public Health: Predict restaurant health risks by combining Yelp data with city inspection records to prioritize inspections.
    • Conservation: Automated wildlife identification in video footage via a global algorithm challenge and an open-source application built on the winning model.
    • Development: Analysis of mobile money transaction records to understand user behavior and inform new service designs for low-income populations.

• Community of data scientists

  • Global community participating in competitions to apply data science and AI skills to social challenges.

Use Cases

• Organizations seeking:

  • Machine learning solutions to specific social or public-sector problems.
  • Help in leveraging existing data to improve programs, operations, or services.
  • Open, community-driven approaches to model development.

• Data scientists and ML practitioners looking to:

  • Work on socially meaningful problems using real-world datasets.
  • Compete in modeling challenges and benchmark skills.
  • Learn from open-sourced winning solutions and tools.

Pricing

The provided content does not list pricing or plan details for competitions or organizational engagements.

Tags

• Datasets
• Machine learning
• Social impact
• Data science competitions
• Open source