StackOverflow vs Kaggle: A Study of Developer Discussions About Data Science

TL;DR

This study compares developer discussions on StackOverflow and Kaggle, revealing differences in focus, topics, and trends in data science, machine learning, and deep learning discussions across these platforms.

Contribution

It provides a large-scale analysis of 197,836 posts using topic modeling to identify key discussion themes and differences between the communities.

Findings

TensorFlow topics are most prevalent on StackOverflow.

Kaggle discussions focus more on meta and practical aspects.

Interest in Keras is rising while TensorFlow discussions slow down.

Abstract

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.