Teaching Scientists Python David Mertz - Gnosis · PyCon-Cuba 2016 Teaching Scientists Python David Mertz page 8 Styles of pedagogy Different students benefit from differing emphases

http://www.gnosis.cx/pycon-cuba-2016/

PyCon-Cuba 2016 Teaching Scientists Python David Mertz

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Reflections onReflections onteaching Python toteaching Python toworking scientistsworking scientists

[email protected]



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Who am I?

Lead trainer for Continuum Analytics, since I startedthere in May 2015.

… the company that makes the Anaconda Pythondistribution, Bokeh, Numba, Dask, Blaze, and many otherFLOSS technologies.

… co-founder, Travis Oliphant created NumPy, for example.

… Jeff Reback is lead developer of Pandas.

… Lots more amazing folks to work with.



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Things I've done?

Director of the Python Software Foundation for 6 years;chair of PSF Outreach & Education Committee; chair ofPSF Trademarks Committee; chair of PSF/NumFOCUSScientific Python Working Group; chair of PSF Python-Cuba WG.

Wrote the IBM developerWorks column CharmingPython; the Addison-Wesley book Text Processing inPython; some short books on Python for O'Reilly; andvarious related articles.

For 8 years worked for a research lab, D. E. ShawResearch, who built the world's fastest supercomputer,Anton, for doing molecular dynamics.



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Styles of pedagogy

Different students benefit from differing emphases inteaching.

Computer science students and programmers

Financial analysts and quants

Data analysts

Domain area working scientists



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Styles of pedagogy



Enjoy language fundamental. Referencing computerscience concepts useful. Discussions of big-O notation,complexity, and data structures. More library/projectinterest than interactive exploration.


Data analysts




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Styles of pedagogy




Have strong math background, but narrow focus ondomain they work in. Most appeal in teaching toolsand libraries. They love Pandas. Sometimes need tobreak their Excel habits.

Data analysts




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Styles of pedagogy




Data analysts

Tend to focus on libraries specific to their needs(scikit-learn, Statsmodels). Broader domain interestthan quants. Sometimes have to ween them off Excelor SQL-only. Interactive exploration highly useful.




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Styles of pedagogy




Data analysts


Most fun to teach because of nimbleness of thought.Can generalize across domains, tools, etc. Notnecessarily in-depth knowledge of computer scienceor some applied math, but intellectually curious.



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Teach the students, not the curriculum

As in war, so in pedagogy: the “facts on the ground”are never exactly what plans anticipated.

Pay attention to what the students actually in front ofyou need, not what their managers said they need.

Where possible, survey students before class starts.

Get short written feedback during class.



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Once class actually starts, the background of students andthe technologies and techniques of most interest to themare probably different than you planned for.






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The people you talk to in planning a course are rarely thesame people you actually teach. For scientists especially, itis better to pay attention to what the students believe theyshould learn.





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It's never a perfect match for the actual classroom, but awell-designed survey (on paper or online) of studentbackgrounds is very useful in tailoring a course accurately.




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A very nice trick is to do a two question, anonymous surveyof all the students during breaks: (1) What is working best?(2) What is not working?



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Types of abstraction

We teach many very smart students. But in broadstrokes, they think differently.

Formal abstraction:

Mathematical generalization of data.

Domain abstraction:

Application of techniques to different fields.



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Formal abstraction

In broad strokes, the quants and data scientists arestrongest when they need to apply differentmathematical techniques to the “same” type of data theyare familiar with.

Domain abstraction...



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Formal abstraction...

Domain abstraction

Over-generalizing to the same degree, scientists andcomputer scientist are very strong at seeing the“sameness” of data arising from many content areas,even outside of their own specialty. E.g. astrophysics,hydrology, molecular dynamics are all just numbers towhich we can apply algorithms.



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Use Python 3.5

Legacy code doesn't matter for students!

There simply is enough better in 3.x (especially 3.5)that we do a disservice by teaching 2.7.

Teach compatibility shims, not 2.x.



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Use Python 3.5


Yes, they may have to maintain older code, but that is notthe level at which general learning occurs. Make sure theylearn to think Pythonically first, worry about the versiondifferences later.

There simply is enough better in 3.x that we do adisservice by teaching 2.7.

Teach compatibility shims.



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Use Python 3.5


There simply is enough better in 3.x that we do adisservice by teaching 2.x:

Some of the most important things for students to thinkabout are lazy sequences (iterators), asynchronousprogramming (coroutines), Unicode vs. bytes, maybeabstract base classes. In general, everything is cleaner inPython 3.5 than in earlier versions.




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Use Python 3.5


There simply is enough better in 3.x that we do adisservice by teaching 2.x.


Use everything in: from __future__ import ...

Python-future is a great library!from future import standard_librarystandard_library.install_aliases()from future.builtins import (bytes, dict, int, list, object, range, str, ascii, chr, hex, input, next, oct, open, pow, round, super, filter, map, zip)



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Use Python 3.5


There simply is enough better in 3.x that we do adisservice by teaching 2.x.


Use almost everything in: from __future__ ...from __future__ import barry_as_FLUFLfrom __future__ import braces



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Customizing courses

Continuum developed an in-house tool to quicklypiece together learning material into courses tailoredfor a particular student group.

Name: Scientific Programming using AnacondaREADME: ./examples/README.py4sci.mdnotebooks: footers: [./templates/Footer.ipynb] headers: [./templates/Header.ipynb] notebooks: # Python Language Basics Basic Python: [ip_essentials, ip_datatypes, ip_ex_attributes]template: {class_date: 2016-04-04, instructor: David Mertz, Ph.D.}



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Jupyter notebooks

Interactive coding as extended REPL

Literate programming

Inline visualizations

Course as a shared project

Students keep personal work from class



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Jupyter notebooks


Notebook cells are each little text editors that can also beexecuted in an IDE-like fashion. Moreover, the variablesdefined in one cell are available throughout the samenotebook.







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Jupyter notebooks



Code can be interspersed with textual explanations orpedagogical background. This code can even includeequations rendered from LaTeX.






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Jupyter notebooks




Graphics generated by matplotlib or Bokeh can appear asoutput cells within notebooks. Static graphics can also beincluded.





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Jupyter notebooks





Developing the instructional notebooks partially inresponse to student questions make the final product feellike a collective effort.




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Jupyter notebooks






Each student enhances her own copies of notebooks tocomplete exercises, experiment with language constructs,understand algorithms, etc. This acts as partially self-created reference after class.



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Reflections on teachingPython to working scientists

Questions?Questions?

[email protected]

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Teaching Scientists Python David Mertz - Gnosis · PyCon-Cuba 2016 Teaching Scientists Python David Mertz page 8 Styles of pedagogy Different students benefit from differing emphases