About the challenge

Turning Data Into Decisions. Building Solutions That Matter.

DataNova 2026 – Global Data-thon is an international, virtual innovation challenge that brings together students, researchers, and working professionals to solve real-world problems through Data Science, Artificial Intelligence, Machine Learning, and data-driven innovation.

Participants are invited to transform ideas into practical, impactful solutions that address challenges across industries and society. Whether you're building predictive models, developing intelligent applications, uncovering insights from complex datasets, or designing data-driven strategies, DataNova is an opportunity to demonstrate your technical expertise, creativity, and problem-solving abilities.

• Artificial Intelligence and Machine Learning

• Data Science and Advanced Analytics

• Generative AI and Responsible AI

• Data Engineering and Business Intelligence

• Cybersecurity and Fraud Detection

• Healthcare and Social Impact

• Sustainability and Environmental Innovation

• Operations Research and Decision Intelligence

• Business Innovation and Open Innovation

We encourage solutions that demonstrate a clear problem statement, sound technical methodology, originality, practical feasibility, responsible technology use, and measurable potential impact.

Projects will be assessed against the competition's published judging criteria. Participants should clearly communicate their approach, implementation, validation, and the value their solution creates.

DataNova 2026 is more than a competition—it's a platform to experiment, collaborate, and demonstrate how data can drive meaningful change.

Bring your ideas. Build your solution. Turn data into decisions.

Ready to participate in DataNova 2026 – Global Data-thon? Follow these steps to prepare your submission.

1. Register for the challenge

Complete your registration through the official competition listing on Devpost and review the eligibility requirements, rules, and deadlines.

2. Choose a problem and track

Identify a real-world problem aligned with one of the challenge tracks. Define the problem clearly, identify your target users, and explain why your proposed solution matters.

3. Build your solution

Develop your project using appropriate technologies, datasets, analytical methods, or AI/ML techniques. Focus on originality, technical quality, feasibility, and responsible use of data.

4. Prepare your submission

Create a complete project submission that includes:

• Project title and overview: Explain what your solution does.

• Problem statement: Describe the challenge you are addressing.

• Solution and methodology: Explain your approach, technologies, and implementation.

• Demonstration: Include a working prototype, demo video, screenshots, or other supporting material, as applicable.

• Impact and validation: Explain how the solution works, how you tested it, and its potential real-world value.

• Team contributions: Clearly identify team members and their respective contributions.

5. Submit before the deadline

Review the official Devpost challenge page for the final submission deadline, required deliverables, and applicable rules. Ensure all links and supporting materials work before submitting.

6. Present your work

Shortlisted participants may be invited to present or demonstrate their projects during the evaluation or final round, subject to the official competition schedule.

• Solve a clearly defined and meaningful problem.

• Demonstrate your own work and explain your technical decisions.

• Use reliable evidence to support your results and claims.

• Follow data privacy, security, licensing, and responsible AI practices.

• Make your project easy for judges to understand and evaluate.

We look forward to seeing your ideas and the solutions you build.

Build. Innovate. Make an Impact.

• Innovation & Originality 20%
  Novelty of the idea, creativity, and differentiation from existing solutions.
• Technical Implementation – 25%
  Technical complexity, quality of execution, appropriate use of tools, and functionality.
• Problem Relevance – 15%
  Clarity of the problem statement, importance, and alignment with the selected track.
• Data & Analytical Rigor – 15%
  Quality of data usage, analytical methodology, model validation, and reliability of results.
• Impact & Feasibility – 15%:
  Practical applicability, scalability, and potential real-world or societal impact.
• Presentation & Documentation – 10%
  Clarity of explanation, demonstration quality, and completeness of supporting documentation.