Hi,
I do a similar thing. I usually start by running a simple statement
(print("Hello") or something alike) from the Python interpreter, then from
IPython, and then from the Jupyter notebook. (This was more important
before the renaming of IPython notebook to Jupyter).
Every time I open and close the corresponding tool, noting the differences
when pressing "Enter". This allows me to stress the difference between
Python, the language, and python, the program, and the rest of the tools.
This is useful if later in the class if I have to open/restart the
notebook. It good to note here what's Anaconda too.
Then I introduce arithmetic operators with a simple statement (say 1+1) and
then variable/names to save the result of the operation (say two = 1 + 1)
and briefly talk about the memory, objects and names.
This is very quick ~10 mins, but allows you to set some vocabulary (object,
name, statement, operator, ...) and some idea of what's happening
memory-wise which you may need later.
Then I follow the lesson importing numpy. I take advantage of the previous
definitions and usually say here that numpy is just another name which
refers to a bunch of code that is now loaded in memory and that was
installed as part of Anaconda.
Best,
Ivan
On Thu, May 5, 2016 at 5:50 PM, Byron Smith <[email protected]> wrote:
> After my first time teaching python-novice, I decided to always skip the
> first section. Instead I work it in later while showing the use of
> packages.
>
> Although I understand why the Python lesson is structured the way it is,
> and I like the idea of an application-first approach, this particular
> implementation has been difficult for learners in my experience. I look
> forward to seeing the new lesson as it takes shape.
>
> Instead I have had great success with the more traditional, "bottom-up"
> approach. Luckily, that's what you end up with when you remove section one
> from the current lesson.
>
> While my experience contradicts the general SWC belief that material needs
> to be motivated early, I've found learners to already believe that they
> should learn Python, making a focus on motivation less important. This is
> probably not the case for the shell and git lessons.
>
> -Byron
>
> On Thu, May 5, 2016 at 4:35 PM, Maxime Boissonneault <
> [email protected]> wrote:
>
>> Hi Alexandre,
>> I find that starting with Numpy is great, because it allows to do
>> interesting things without the need for introducing weird and boring
>> concepts like loops, variables or conditionals.
>> Students immediately can relate to things they know like seeing a
>> picture, or seeing a table of numbers.
>>
>> Starting with abstract concepts like variable naming, loops,
>> conditionals, etc, is bound to bore students, which is never a good thing
>> at the very beginning of a class.
>>
>> Cheers, and have a good first time!
>>
>>
>>
>> --
>> ---------------------------------
>> Maxime Boissonneault
>> Analyste de calcul - Calcul Québec, Université Laval
>> Président - Comité de coordination du soutien à la recherche de Calcul Québec
>> Team lead - Research Support National Team, Compute Canada
>> Instructeur Software Carpentry
>> Ph. D. en physique
>>
>>
>> On 2016-05-05 15:16, Alexandre Manhaes Savio wrote:
>>
>> Hi,
>>
>> I am preparing for the first time the python-novice-inflammation lesson.
>> I am also going to add in the beginning instructions on the most popular
>> initial options on how to actually run the python code.
>>
>> I am struck on how the novice lesson directly starts with Numpy without
>> explaining too much variable naming and assignment, for instance. In my case
>> I will begin with students with very little programming experience.
>>
>> I am also aware that Greg is preparing another lesson for python but there
>> is some work there to be usable.
>>
>> Could you please write me your opinion and experience about how you prepare
>> the beginning of the python novice lesson?
>>
>> Thank you very much!
>>
>> Best,
>> Alex
>>
>> Alexandre Manhães Savio
>> PhD, Medical Imaging, Machine Learning
>> Klinikum rechts der Isar, TUM, Münchenalexsavio.github.io | email:
>> [email protected]
>>
>>
>>
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