I understand the need to minimise prerequisites and certainly the portions of the shell lessons relating to loops, pipes, filters, and scripts are unnecessary overhead. However, given that they will be loading data from files into pandas dataframes, there has to be some concrete concept of where the files are relative to the working directory (even in the Jupyter notebook). This is also going to be an issue with getting the data onto the machines the learners are using and loading them up later.
Anyway, my feeling is that, if this isn't expected as a prerequisite (which is reasonable for most of our novice learners), then we have to figure our how to get that knowledge to stick. At present, there are 10-15 minutes allocated for in the "Reading tabular data" segment; that may be optimistic. If I were to squeeze out something, I'd start with the survey of scipy at the end of the day, followed by testing (as discussed in another issue).
I understand the need to minimise prerequisites and certainly the portions of the shell lessons relating to loops, pipes, filters, and scripts are unnecessary overhead. However, given that they will be loading data from files into pandas dataframes, there has to be some concrete concept of where the files are relative to the working directory (even in the Jupyter notebook). This is also going to be an issue with getting the data onto the machines the learners are using and loading them up later.
Anyway, my feeling is that, if this isn't expected as a prerequisite (which is reasonable for most of our novice learners), then we have to figure our how to get that knowledge to stick. At present, there are 10-15 minutes allocated for in the "Reading tabular data" segment; that may be optimistic. If I were to squeeze out something, I'd start with the survey of scipy at the end of the day, followed by testing (as discussed in another issue).