@mario_angst_sci honestly, it depends on what you mean by "data science":
- these days there are people who spell "data science" and mean #AI 🙄
- on a more serious note, there is the different library ecosystem of R and Python. This is mostly domain-specific. Both, as you know, are pretty generic, until you meet a certain domain (e.g. imputation methods or biological analysis), where one or the other excels.
- Speaking about EXCEL, you might want to drop in https://datavzrd.github.io/ - a viable alternative.
- If you really want to go nuclear, teach workflow management systems ( #Snakemake, #Nextflow). 😉
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@rupdecat oh man, you are so right about the AI point! I think I do have lost touch a bit with what people mean by "data science" and I probably mean "data analysis"...
And I totally agree about the ecosystem, I just think the bread and butter data wrangling/ analysis/ visualization tooling in R (and see how I avoided the term data science here, you were very right that this is actually super helpful), is so much more focused and hassle-free than Python.
My own preferences for R in my specific line of work is (no kidding) probably 90% due to Bayesian modeling packages, while I'll alway reach for Python for NLP.
datavzrd looks incredibly cool! Sort of feels like the interactive complement to https://www.tadviewer.com - I love these focused applications.
And haha, getting into workflow management would be the other direction of going nuclear - but we are actually planning on having a "advanced techniques" outlook session and I was going to talk about targets and snakemake. My currently wrapping up larger project actually runs on Dagster, but I wish I would have known about Snakemake when I set it up...
@mario_angst_sci one more aspect: I learned it the hard way, that you may have more time in curricular courses. There still is a limit to the amount of detail people can digest in one go. 🤷♂️
@rupdecat do I understand you right in that you mean something like "aim for less, but do it right ;)"?
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