* Exploratory analysis (arguably part of model work)
* Results presentation
Then again, this is an ongoing disagreement I have with the Kaggle folks over what constitutes "data science," where I'm pretty confident that "applied machine learning" is a better explanation of what their contests are about.
I'd say data transformation is a part of feature engineering (commonly the bulk of the effort in a ML application). And exploratory analysis is part of model work. W/o those 2 one would be building a model out of dreams and wishes.
Data Science is probably a poorly chosen description. I'd say common use includes infrastructure work which for most of us consists in engineering work.
