Coding habits for data scientists

If you’ve tried your hand at machine learning or data science, you know that code can get messy, quickly. Typically, code to train ML models is written in Jupyter notebooks and it’s full of (i) side effects (e.g. print statements, pretty-printed dataframes, data visualisations) and (ii) glue code without any abstraction, modularisation and automated tests. While this may be fine for notebooks targ

[wata88]

共感はあるが、本番前なら致し方ない感じもある