There are two parts to the question. One is about the software. And one is about a course to learn the appropriate statistical analysis.
If you don't have a background in statistical analysis or programming, I would recommend starting with gui-based software, like Jamovi, which is free, and will do common analyses. But there's a limit to the kinds of analyses it can do.
If you need specialized analyses specific to botany, there may be specific packages for R that conduct these. R isn't as difficult to use as it first seems. But it depends on what you need.
If you have access to SPSS, it's pretty easy to use for basic analyses. Same with SAS. But these are expensive programs if you don't have access to them.
Personally, I don't recommend Python for statistical analysis. I've been working with it recently for that purpose, and all I keep saying to myself is, "This is so much easier in R."
If you need to learn basic analysis of experiments, look for terms like "analysis of experiments" or "design and analysis of experiments". Some courses that focus in R spend a lot of time on the language, which may not be important for what you need.
If you are just starting out in analysis of experiments --- like you don't know what a t-test or a chi-square test is used for --- you might start with some free resources. These might not be entirely rigorous, but you might get you started enough to understand some basics.
Examples:
Handbook of Biological Statistics: https://www.biostathandbook.com/
Summary and Analysis of Extension Program Evaluation in R (with the caveat that I am the author): https://rcompanion.org/handbook/
And I have a list of free statistics books here: https://rcompanion.org/handbook/A_04.html
I like your list of books, Sal Mangiafico. Here are some more you might consider adding.
1) Course notes from some of Penn State's online courses: https://www.worldcampus.psu.edu/degrees-and-certificates/penn-state-online-applied-statistics-masters-degree#courses
2) Doug Bonett's Statistical Methods for Psychologists: https://dgbonett.sites.ucsc.edu/statistical-methods-for-psychologists/
Note that the latter includes R examples for all 5 chapters, and SPSS examples for 3 of the 5 chapters.