As a biomedical researcher who prefers using python & R over Excel: (1) Graduate students (who are the bulk of the scientific labor force) usually have some tr

As a biomedical researcher who prefers using python & R over Excel: (1) Graduate students (who are the bulk of the scientific labor force) usually have some training in python, R, or Matlab, but are seldom practiced in applying it to real-world work. So if a lab standardizes on python, R, etc. the senior people have to do a lot of extra work to support the programming, rather than doing work with intrinsic scientific value. It's easier to teach the Excel quirks than to teach effective programming practices. Excel quirks are concrete, and "good practices" in programming are vague and situational. (2) Python & R have their own pitfalls. It's easy to apply the wrong filters to a data set and compare subsets that aren't what you think you're comparing. Although when they go wrong, they go very wrong, and this is easier for a supervisor to notice. (3) Excel has rich text formatting, graph embedding, and data types, so it's very useful as a human-readable data interchange & summary format even if you never do computation in it. IMO neither programming languages nor Excel are a great fit for data analysis. Something purpose-made, like JMP or even GraphPad, is probably the better choice in most situations. Programming gets you automation but at the cost of high complexity. Since you still need to look at and think about the data there's a limit to how much time automation can save (Amdahl's law).