EY announces winners of EY NextWave Global Data Science Challenge launched to connect STEM students worldwide
- Ongoing global competition established to create network of students tasked to solve some of the world's most pressing business problems
- Winners from Argentina, Germany, Singapore and the US joined EY executive leaders in New York for awards ceremony
NEW YORK, June 14, 2019 /PRNewswire/ -- EY today announces the launch of the initiative to connect Science, Technology, Engineering and Mathematics (STEM) students worldwide, with the winners of the first EY NextWave Global Data Science Challenge.
The Challenge provides STEM students with the opportunity to analyze real-world problems through data and develop initiatives that will help build a better working world. Top participants received an EY Badge, recognizing their knowledge and skills in data science. The EY Badges program supports EY people in shaping their own careers by earning credentials in skills that differentiate them in the market, such as data analytics or artificial intelligence (AI).
For this year's Challenge, the students used real-world data from Skyhook, a pioneer in location technology, to help tackle the task of smart mobility in cities as the urban population rises across the globe. Future challenges will continue to mirror the EY focus on building a better working world for all people, clients and communities.
The EY NextWave Global Data Science Challenge attracted more than 4,500 students from nearly 500 universities. A judging panel comprised of EY leadership and data science professionals assessed in-person presentations of more than 50 of the top performing teams in the competition. The three global winners were chosen among those who combined the best results with in-depth insights and contributions to building a better working world. Of the 12,000 entries received, the 2019 EY NextWave Data Science Challenge winners are:
First place: Sergio Banchero. Sergio is from Argentina, pursuing his Master's degree in Data Science at the University of Western Australia in Perth. Sergio analyzed the data created by devices as they moved from the city center to the city border. He created models that could be used for real-time decision making, such as identifying issues in transportation networks; identifying blocked roads and the impact on city traffic; and identifying common travel patterns to extend and enhance the public transportation network.