Since its creation, the Data Science Game gathered more than 2500 students from all around the world to compete on deep learning, text-mining and predictive modelling algorithms to defend the colours of their university. For its fourth edition, more than 30 countries were represented. During one month, students in data science faced a real-world, demanding and innovative business challenge for the qualification phase. The final phase, held in Paris, saw the 80 best students fiercely competing for the title of Data science Champion.
Awarding the students who succeeded in placing their Data Science skills at the service of innovative business issues.
To qualify for the finals, the teams helped BNP Paribas improving its customer service using Machine Learning algorithms to predict customer interest in specific bonds available in the market.
Again this year, the competitors handled the complexity of a real-life business issue by taking into account the differences between the customers. Indeed, investors may be interested in a specific category of bonds, or may have different strategies.
Overall, the students had to mobilize their machine learning skills and be creative in order to capture rare events. This qualifying challenge gave the students the opportunity to prove their worth and their ability to build innovative methods to offer to businesses the best of Machine Learning and Data Science.
The 20 best data science universities worldwide supported by key contributors to the field of Data Science, competed to conceive and implement the most innovative and efficient predictive algorithm.
During the finals, the “crème de la crème” of data science education gave the best of their data science knowledge and programing experience to get the highest predictive scores and to help e-commerce industry transforming its business. The finalists faced difficult challenges, mixing sequential data and unstructured information. Students had to handle navigation tracking data to predict the probability that a purchase action occurs before the end of the navigation.
This year once again, Data Science Game was a resounding success thanks to its partners’ support, who are all key contributors in the field of Data Science. For the final phase, the students were welcome in the “Château Les Fontaines”, the training and development campus of the Capgemini Group. Microsoft provided its support by giving free and full access to its Azure services while the final challenge was set by Cdiscount. A 30 hours hackathon witnessed the creation of some of the most innovative and refined solutions to the problem, : the best team achieved a score (log-loss) of 0.2540. To assess teams, Data Science Game used this year a very robust and well designed open source challenge platform called Qscore (developed by Zelros and deployed on Microsoft Azure for the finals).
In this challenge, the best teams distinguished themselves thanks to smart and efficient feature engineering taking into account the complex sequential economic paths and constraints, and a deep knowledge of machine learning and statistics to compound the different models into powerful approaches. To stand out in this challenge, our 80 ambassadors had to find the most innovative algorithm while managing their time and team work.
The Data Science Game, offering international opportunities for recruitment and leadership recognition, is an association that promotes the development of data science and skills related to scientific challenges addressed to students in computer science, data science, engineering, statistics and/or applied mathematics. Our team is mostly composed of volunteer data scientists who are working hard to make the Data Science Game a unique and wonderful challenge for students and partners.