Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems
Fuente:
arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866917964857475072 |
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| author | McGowan, Lucy D'Agostino Tass, Shannon Tyner, Sam Wang, HaiYing Yan, Jun |
| author_facet | McGowan, Lucy D'Agostino Tass, Shannon Tyner, Sam Wang, HaiYing Yan, Jun |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_20281 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems McGowan, Lucy D'Agostino Tass, Shannon Tyner, Sam Wang, HaiYing Yan, Jun Other Statistics 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. |
| title | Data Jamboree: A Party of Open-Source Software Solving Real-World Data Science Problems |
| topic | Other Statistics |
| url | https://arxiv.org/abs/2502.20281 |