I understand what you're saying. I agree that there are situations where it's very useful, and I defer to you as having more recent experience regarding what th

I understand what you're saying. I agree that there are situations where it's very useful, and I defer to you as having more recent experience regarding what those are. However, it isn't just the "tasks that I have at hand". Half of my point is that, even in data science contexts, one ought often to minimize the amount of work done in a notebook. To take your scenarios, I agree with "pulling data from a DB and plotting it". But I don't agree with "analyzing an experiment". I may not do data science now, but I did before. "Analyzing an experiment" involves debugging, and it is important that the analysis is correct, and repeatable for publication/distribution. So I do maintain that for any non-throwaway code, everyone is well-served by embracing traditional engineering discipline for the debugging and verification challenges that will inevitably crop up.