Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems
Abstract
The evolving focus in statistics and data science education highlights the growing importance of computing. This paper presents the Data Jamboree, a live event that combines computational methods with traditional statistical techniques to address real-world data science problems. Participants, ranging from novices to experienced users, followed workshop leaders in using open-source tools like Julia, Python, and R to perform tasks such as data cleaning, manipulation, and predictive modeling. The Jamboree showcased the educational benefits of working with open data, providing participants with practical, hands-on experience. We compared the tools in terms of efficiency, flexibility, and statistical power, with Julia excelling in performance, Python in versatility, and R in statistical analysis and visualization. The paper concludes with recommendations for designing similar events to encourage collaborative learning and critical thinking in data science.
Cite
@article{arxiv.2502.20281,
title = {Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems},
author = {Lucy D'Agostino McGowan and Shannon Tass and Sam Tyner and HaiYing Wang and Jun Yan},
journal= {arXiv preprint arXiv:2502.20281},
year = {2025}
}