  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.
