Data Competitions: Crowdsourcing with Data Science Platforms

Abstract

With the rise of artificial intelligence and machine learning, competitive data science platforms like Kaggle are gaining momentum. From a host's perspective, the platforms offer access to a large crowd of data scientists who can solve their data science problems efficiently and cost-effectively. From the participant's perspective, the platforms provide the opportunity to apply their skills to real-world problems, interact with other data scientists, and win prizes. The chapter provides an overview of competitive data science platforms and assesses their potential for business and academia. A series of opportunities and challenges of data competitions are outlined, and a concrete case is illustrated. The chapter also demonstrates common pitfalls that hosts of data competitions need to be aware of by discussing the relevance of problem definition, data leakage, and metrics to evaluate different solutions.

Citation

Zimmermann, J.L. (2021), "Data Competitions: Crowdsourcing with Data Science Platforms", Einhorn, M., Löffler, M., de Bellis, E., Herrmann, A. and Burghartz, P. (Ed.) The Machine Age of Customer Insight, Emerald Publishing Limited, Leeds, pp. 183-197. https://doi.org/10.1108/978-1-83909-694-520211017