Applications call for Ernst & Young (EY) Better Working World Data Challenge 2022. Calling all university students and early career data scientists – get ready for the 2022 Better Working World Data Challenge.

Benefits

• Students get free access to Azure, Microsoft’s cutting-edge technology stack, as well as free Microsoft IT certifications.
• Also, access to tutorials and training.
• The opportunity to collaborate with others around the world.
• In addition, the chance to win up to $10,000 in cash prizes.
• The top 30 finalists in each challenge will also win mentoring from EY.

Challenges

Choose from three available options, from beginner to advanced – all are welcome. Curated learning programs from EY, Data Camp as well as Microsoft to will help you prepare.

Level 1: Build a Species Distribution Model for the frog species – “Litoria Fallax” across Australia using TerraClimate variables.

Level 2:  develop a Species Distribution Model for predicting the frog occurrence for 9 frog species across Australia, Costa Rica, and South Africa using a variety of open-source geospatial datasets available on Microsoft Planetary Portal

Level 3: Build a combined regression model to predict the density of frog population for regions covering Australia , South Africa and Costa Rica.

Eligibility

Any university student with an interest in data science can enter and participate. If you have less than two years of professional experience then you can also join the Challenge, but you will need to pay to use Azure.

Submission Process

• Participants can use whatever technologies and/or methodologies they feel would be most appropriate for the challenge at hand. You must submit the results online on the platform.
• If you are selected as a finalist, you will be provided instructions on how to submit your results and supporting documentation.
• Finalists should also submit a video presentation of their work. Evaluation criteria will be available after the announcement of the finalists.
• You will also be required to provide any code and external datasets leveraged, so be sure you have used only publicly available data and that your code and methodology are robust and repeatable.