This is the part I somewhat disagree. I've seen lots of strong computational biologists make the leap into generic data science, but I've seen way too many CS/data science types struggle. They take the data at face value, not recognizing the fact that biological data has flaws. A sound understanding of biology/chemistry helps a lot with identifying those flaws and generally with designing experiments/research.
Admittedly that's a bit of a generalization and I am sure there are a decent number of exceptions but consistent with my experience.
Admittedly that's a bit of a generalization and I am sure there are a decent number of exceptions but consistent with my experience.