Programming

IIRC there were some polls for how helpful LLMs were by language/professions, and data science languages/workflows consistently rated LLMs very highly. Which makes sense, because the main steps of 1) data cleaning, 2) estimation and 3) presenting results all have lots of boilerplate.

Data cleaning really just revolves around a few core functions such as filter, select, and join; joins in particular can get very complicated to keep track of for big data.

For estimation, the more complicated models all require lots of hyperparameters, all of which need to be set up (instantiated if you use an OOP implementation like Python) and looped over some validation set. Even with dedicated high level libraries like scikit, there is still a lot of boilerplate.

Presentation usually consists of visualisation and cleaning up results for tables. Professional visualisations require titles, axis labels, reformatted axis labels etc, which is 4-5 lines of boilerplate minimum. Tables are usually catted out to HTML or LaTeX, both of which are notorious for boilerplate. This isn't even getting into fancier frontends/dashboards, which is its own can of worms.

The fact that these steps tend to be quite bespoke for every dataset also means that they couldn't be easily automated by existing autocomplete, e.g. formatting SYS_BP to "Systolic Blood Pressure (mmHg)" for the graphs/tables.