DSLE: A Smart Platform for Designing Data Science Competitions


During the last years an increasing number of university-level and post-graduation courses on Data Science have been offered. Practices and assessments need specific learning environments where learners could play with data samples and run machine learning and data mining algorithms. To foster learner engagement many closed-and open-source platforms support the design of data science competitions. However, they show limitations on the ability to handle private data, customize the analytics and evaluation processes, and visualize learners' activities and outcomes. This paper presents Data Science Lab Environment (DSLE, in short), a new open-source platform to design and monitor data science competitions. DSLE offers a easily configurable interface to share training and test data, design group works or individual sessions, evaluate the competition runs according to customizable metrics, manage public and private leaderboards, monitor participants' activities and their progress over time. The paper describes also a real experience of usage of DSLE in the context of a 1st-year M.Sc. course, which has involved around 160 students.

2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC)