Something that almost always gets left out of these discussions is how quickly you can spin a new dataset into a highly polished, ready-to-print-for-executive-r

Something that almost always gets left out of these discussions is how quickly you can spin a new dataset into a highly polished, ready-to-print-for-executive-review report IF YOU HAVE MASTERED EXCEL HOTKEYS. For context, I have meaningful experience doing data analysis using various combinations of SQL, python, ruby, R/tidyverse, and excel with lightning-fast fingers. In a previous role, I regularly needed to summarize new datasets to executives, and I found that nothing was quicker and more flexible than the following workflow: copying and pasting raw data into an unformatted "data" sheet, creating a new sheet from a well-formatted template, and quickly writing whatever "SUMIFS" prototypes I needed then filling out the rest of the sheet by copying those formulas across cells. Often excel users are stereotyped as having error-prone, sporadic workflows where they are often mistakenly writing over raw data, etc. And that is often a valid stereotype. However, I believe that there exist scenarios where excel is part of the optimal workflow.