We are delighted to be sponsoring The Data Science Game for the second year in a row.
For the second year in a row, the Data Science Game will see Data Science students from around the world compete in this prestigious competition. This year, 143 teams, representing more than 50 universities1 f rom 28 different countries will face a reallife demanding and innovative business challenge . To stand out in this competition, students will have to imagine and implement predictive models related to Big Data issues. On September 10 and 11, Paris will welcome an international student hackathon focused on Big Data analytics. From the 143 teams, twenty groups from around the world will defend their university’s reputation in the 2016 Data Science Game.
But to access this ultimate challenge, teams who have already enrolled first have to demonstrate their worth through an online application process from June 17 to July 10. During this first phase, coorganized with ChaLearn specialists in the organization of Machine Learning challenges, the applicants will have to find the best solutions to a predictive problem involving massive and complex data, using statistical algorithms to treat and figure out this data. Each university will be scored on the predictive power of these algorithms, and a ranking will single out the best twenty teams that will compete in the final challenge in Paris. This year once again, Data Science Game can count on its partners’ support, who are key contributors in the field of Data Science. Thanks to Capgemini, a global leader in digital consulting and IT services, participating students will have the opportunity to stay in an exceptional historic place: « Les Fontaines » (the Capgemini Group Campus) near Paris . John Brahim, Head of Capgemini Group’s Insights & Data Global Practice said "Data analytics are critical to our clients in this digital landscape, providing powerful insights that change the business. Competitions such as the 2016 Data Science Game will help inspire the next generation of data specialists and to provide them with the environment to experience firsthand the complexities of solving real business challenges. We hope that this will encourage them to go on to pursue a stimulating career in data analytics."
When Computer Vision serves renewable energy production
This year, the qualification challenge of the Data Science Game has focused on concerns for the future, at the intersection between ecology and energy issues; the trial looking at optimizing the production of solar energy.
In order to map the solar energy production potential in France, the OpenSolarMap project provides satellite images of roofs of 80,000 buildings. Based on the individual contributions of users, the orientation of about 15,000 roofs has been categorised. Automatic classification of roof orientation is a true challenge for Etalab, the French public agency in charge of open data and use of data in the administration in France and which provided the data.
For the contestants of the Data Science Game, the challenge was to develop an algorithm able to recognise the orientation of a roof from a satellite photograph by building on more than 10,000 photograph of roofs which have been categorized thanks to crowdsourcing.