Dear Bianca, I agree with Paul's comment that "biostatistics" is a very broad thing to aim for.
>From several years of doing R training to biologists (postgraduates, PhDs, >postdocs), I would say that a really important skill that seems lacking is >getting good at manipulating data and doing exploratory data analysis - in >other words, learning to ask questions from data. This is the essence of the "Data Carpentry with R" workshop: https://datacarpentry.org/R-ecology-lesson/ Although it's called "ecology" lesson, it's really about learning principles of "tidy" data structures, how to manipulate those data and visualise them in a number of ways. I think getting people good at exploratory data analysis is, by itself, very powerful, regardless of the field people later work on (biostatistics or not). In any case, this tour de force from Susan Holmes and Wolfgang Huber might be helpful to choose the focus for such a course: https://www.huber.embl.de/msmb/ (but it also illustrates how broad the field is) Regarding SPSS vs R, I would say that if the focus is biostatistics, the existence of R/Bioconductor <https://bioconductor.org/> should suffice to make a very strong argument for R. (plus learning a scripting language encourages reproducibility skills as mentioned above, which I think SPSS does not easily offer). Hope this helps! hugo ------------------------------------------ The Carpentries: discuss Permalink: https://carpentries.topicbox.com/groups/discuss/T7aec92fdeda8ec4b-Mcc9c1abeb710cfb0ddacb0f8 Delivery options: https://carpentries.topicbox.com/groups/discuss/subscription
