Analyze, understand, and create. DataSci '17 is a data-science competition open to all undergraduate/graduate students globally.

DataSci is an online competition aiming to support student-created data science projects. The competition will be open to all university students globally, and will involve the following topics: creating unique algorithms for analyzing data, visualizations, write-ups on derived insights, and more. Our submission platform will open March 1st, and will close on April 8th.

Participants will work together in small teams to find undiscovered patterns and new meanings in datasets of their choice. General guidelines will be provided, but DataSci will primarily be about engaging both the creativity and learning capacity of its participants. We highly recommend reading through the 'Rules' section of DataSci '17 to find out more information this competition. 

Looking for a dataset? We highly recommend the following collections:

View full rules

Eligibility

  • Student at a university (undergraduate or graduate)

Requirements

  • Must complete the Typeform provided on the home page.
  • Must submit the project before April 8th.
  • Project submitted MUST relate to data analysis, data science, or the like, in some way or form.

How to enter

To enter, you MUST have signed up on a forthcoming Typeform link, which will be available soon. Also, please make sure you've read the 'Rules' page in its entirety before submitting a project. 

Judges

Abhishaike Mahajan

Abhishaike Mahajan

Judging Criteria

  • Write-up
    We understand that the 'interesting-ness' of a data-science project often doesn't come from the data, but from the analysis write-up following it. Thus, the questions we ask in the submission form will be the primary way we decide the winners.
  • Question solved/attempted to solve
    Solving very interesting/useful/unusual questions, using data, will be a large factor in the finalists in DataSci '17.
  • Novelty of dataset used
    This is weighted low, relative to the other criteria, but we will look favorably upon interesting datasets used in your project submission.