-- visualize python code execution line-by-line:

https://github.com/lgpage/nbtutor

-- python 3 for scientists:

http://python-3-for-scientists.readthedocs.io/en/latest/

-- restructure it all:

after basic introduction class, have students run through ipython

notebooks before class, and then in class, have a workbook that we

will fill in together, interactively

-- Machine learning: http://www.toptal.com/machine-learning/machine-learning-theory-an-introductory-primer

-- post an "assignment" of tasks after each class to the discussion forum.

-- try git:

https://try.github.io/levels/1/challenges/1

-- python in HPC:

https://github.com/pyhpc/pyhpc-tutorial

-- new-style classes:

http://python-history.blogspot.com/2010/06/new-style-classes.html

-- decorators: http://thecodeship.com/patterns/guide-to-python-function-decorators/

-- use project Euler for in-class code projects

-- piazza or freeforums.org -- open discussion forum

-- tedEd -- exercises embedded in lectures

-- jiffylab -- set up a teaching environemtn

or ipython chrome app (colaboration ?)

-- do the codeacademy python track outside of class?

-- Stanford NIFTY: computer science projects for students

-- ipython widgets

-- wakari: host a notebook

-- plot.ly

-- coLaboratory Notebooks

-- available in the Chrome store

-- pythran?

-- nbdiff

-- mpld3 -- interactive matplotlib in ipython notebook

-- "seven ways to think like the web"

-- have a lecture at the start on "mananging projects with git, github, pull

requests

-- debugger: from Nathan on yt:

"The easiest way to debug issues like this is to use a python

debugger. I prefer ipdb, which has nice integration with IPython,

including tab completions. Others like pudb. There's also pdb,

which is part of the python standard library. All of these

debuggers have a gdb-like command-line interface, allowing printing

out backtraces, inspecting variables, and stepping through

code. Both ipdb and pudb can be installed via pip."

references:

-- "Computational Thinking" J M Wing (Communications in the ACM)

-- Computing 4 Physics: https://github.com/Computing4Physics/C4P

grading:

-- grading is based on discussion. Here's a loose metric:

by posting, I don't mean a "me to" type post, but something

either demonstrating a problem you don't understand (giving code),

asking for some detail from the lecture to be explained,

sharing a neat trick you found

A: 12 postings to the discussion board (essentially once a week)

+

a small example showing how you can apply what we've discussed

in class to your field.

A-: 8 postings to the discussion board

B+: 5 postings to the discussion board

B: 2 postings to the discussion board

-- hadoop streaming API: http://www.michael-noll.com/tutorials/writing-an-hadoop-mapreduce-program-in-python/