> The simplest thing is to use the 3.8.0 python.org installers. This use > pip to add anything you consider essential.
As mentioned previously, you do need to make sure that they tick the box to add Python to the PATH on windows. It is almost guaranteed someone will not do that and will then have a very hard time figuring out what has gone wrong (happens to me every time I teach). Considering that this is a lecture and not a workshop I'm assuming the students aren't actively installing and running python while you are teaching. In which case, whilst I would mention pip, I would probably just have the required libs preinstalled on my computer ready to go. Learning pip is easy to do and if they're interested later can be taught separately. But watching someone installing packages on the terminal is not very interesting. On the other hand if the students are actively following along and running python within the lecture then they will obviously need to be shown how to do this. Also whilst the remit of the lecture is to showcase how to do statistics in Python, I wouldn't take this as an absolute limit. I would go through examples of graphs, probably taking inspiration from https://www.tylervigen.com/spurious-correlations and using something like dash (https://dash.plot.ly/). I would also try to show more creative ways of playing with data - for instance I worked on this project dedicated to showing data using GIFs ( https://datagifmaker.withgoogle.com/editor/racetrack - don't look too hard at the representations though). But instead of spending a whole lecture explicitly on statistics I would probably use the last 10 minutes showcasing other uses of Python which are (apologies to those who find statistics utterly encapsulating) a bit more interesting. For instance I have a <200 LOC game of pong (technically a _graphical_ user interface) which is usually fun to showcase ( https://gitlab.com/ndevox/pygame-pong/blob/master/pong.py). I'd also be tempted to show off things like websites (which could display statistics publicly), chatbots (which, if using something like an NLTK classifier, are essentially statistical machines) etc. Think about what interests you the most and see if you can display it on the screen in some way. Essentially whilst it is very important to show them to make graphs in various ways, you'll probably struggle to captivate the entire audience with this. Whereas ending with some slightly wilder but more enticing examples can make those who weren't interested in the statistics want to pay more attention to what you have been saying. - Nick On Wed, Nov 20, 2019 at 11:33 PM MRAB <pyt...@mrabarnett.plus.com> wrote: > On 2019-11-20 21:58, Terry Reedy wrote: > > On 11/20/2019 11:09 AM, Göktuğ Kayaalp wrote: > > > >> The first problem is installation: apart from me, a Debian user, > >> everybody has Windows or Mac laptops, and IDK how you install Python on > >> them. > > > > The simplest thing is to use the 3.8.0 python.org installers. This use > > pip to add anything you consider essential. > > > For Windows, I use "Windows x86-64 executable installer" for 64-bit and > "Windows x86 executable installer" for 32-bit from > https://www.python.org/downloads/windows/. > -- > https://mail.python.org/mailman/listinfo/python-list > -- https://mail.python.org/mailman/listinfo/python-list
