  Background: Meeting the growing industry demand for Data Science requires
cross-disciplinary teams that can translate machine learning research into
production-ready code. Software engineering teams value adherence to coding
standards as an indication of code readability, maintainability, and developer
expertise. However, there are no large-scale empirical studies of coding
standards focused specifically on Data Science projects. Aims: This study
investigates the extent to which Data Science projects follow code standards.
In particular, which standards are followed, which are ignored, and how does
this differ to traditional software projects? Method: We compare a corpus of
1048 Open-Source Data Science projects to a reference group of 1099 non-Data
Science projects with a similar level of quality and maturity. Results: Data
Science projects suffer from a significantly higher rate of functions that use
an excessive numbers of parameters and local variables. Data Science projects
also follow different variable naming conventions to non-Data Science projects.
Conclusions: The differences indicate that Data Science codebases are distinct
from traditional software codebases and do not follow traditional software
engineering conventions. Our conjecture is that this may be because traditional
software engineering conventions are inappropriate in the context of Data
Science projects.
