[gundmc]

To be fair, linear code is often totally sufficient for most types of data analysis. Biologists don't really need to understand design patterns or polymorphism, they just need to not make computational mistakes when transforming the data.

[shpongled]

Absolutely. My point was more than you can't expect comp. biologists to actually be "good" programmers when compared to SWE or even web devs.

Most of the code I write to do biological data analysis is fairly linear. However, I also generally use a static type system and modularity to help ensure correctness.

I've perused a lot of code written by scientists, and they could certainly learn to use functions, descriptively name variables, use type systems and just aspire to write better code. I just saw a paper published in Science had to issue a revision because they found a bug in their analysis code after publication that changed most of their downstream analysis.

[oneplane]

It does get rather problematic when you have large quantities of...stuff. You can't run linear stuff in parallel so now you're bound to whatever single CPU core you have lying around.

I'd say that getting some basic data science computing skills should be more important than the silly SPSS courses they hand out. Once you have at least baseline Jupyter (or Databricks) skills you suddenly have the possibility to do actual high performance work instead of grinding for gruntwork. But at that point the question becomes: do the people involved even want that.