StackOverflow与Kaggle：关于数据科学的开发人员讨论研究
StackOverflow vs Kaggle: A Study of Developer Discussions About Data Science
摘要：
软件开发人员越来越需要了解基本的数据科学（DS）概念。最近，机器学习（ML）和深度学习（DL）在用户应用程序开发中的应用急剧增加，无论是通过框架利用还是从头开始实现。这些话题在网络平台上引起了很多讨论。本文通过大规模的定性和定量实验，对来自StackOverflow和Kaggle的197836篇帖子的特征进行了研究。潜狄利克雷分配主题建模是用来提取二十四个DS讨论的主题。主要发现包括TensorFlow相关主题在StackOverflow中最普遍，而Meta讨论主题在Kaggle上最普遍。StackOverflow倾向于包括较低级别的故障排除，而Kaggle则专注于实用性和优化排行榜性能。此外，在这两个社区，DS的讨论正在以惊人的速度增加。虽然StackOverflow上的TensorFlow讨论正在放缓，但对Keras的兴趣正在上升。最后，集成算法是Kaggle中提到最多的ML/DL算法，但很少在StackOverflow上讨论。这些发现可以帮助教育工作者和研究人员更有效地定制和优先考虑研究和向不同开发人员社区传达DS概念的工作。
英文摘要：
Software developers are increasingly required to understand fundamental Data science (DS) concepts. Recently, the presence of machine learning (ML) and deep learning (DL) has dramatically increased in the development of user applications, whether they are leveraged through frameworks or implemented from scratch. These topics attract much discussion on online platforms. This paper conducts large-scale qualitative and quantitative experiments to study the characteristics of 197836 posts from StackOverflow and Kaggle. Latent Dirichlet Allocation topic modelling is used to extract twenty-four DS discussion topics. The main findings include that TensorFlow-related topics were most prevalent in StackOverflow, while meta discussion topics were the prevalent ones on Kaggle. StackOverflow tends to include lower-level troubleshooting, while Kaggle focuses on practicality and optimising leaderboard performance. In addition, across both communities, DS discussion is increasing at a dramatic rate. While TensorFlow discussion on StackOverflow is slowing, interest in Keras is rising. Finally, ensemble algorithms are the most mentioned ML/DL algorithms in Kaggle but are rarely discussed on StackOverflow. These findings can help educators and researchers to more effectively tailor and prioritise efforts in researching and communicating DS concepts towards different developer communities.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
