Community Data Science Workshops (Fall 2014)/Reflections: Difference between revisions

imported>Mako
imported>Mako
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== Session 3: Data Analysis and Visualization ==
 
The goal of the lecture was to walk people through the actual mess of makingwriting code from scratch and focused on a single example of code. that builds a dataset from Wikipedia.
 
In general, goals were clearer this time and the use of Anaconda meant that we could use <code>requests</code> which cleaned up several problems last time and led to more clear code.
=== Afternoon sessions ===
 
One challenge, pointed out in a question at the end of the final lecture, is that we don't actually do very much actual data analysis during the lecture. Next time, we should make this much more clear up front. The reality is that we were doing analysis from the very first day and that where analysis starts and where data cleaning and munging ends can be fluid, fuzzy, and subjective. We should foreground this in the beginning of the lecture or even at the beginning of the workshops.
'''Afternoon of Session 3:'''
 
=== Afternoon sessions ===
'''The spreadsheets session.''' People were modifying the code to build their own dataset and did their own visualizations. At least a few people. That was cool!
 
We ran two sessions this time.
'''The MatPlotLib session'''. Most people in the session were deeply lost. The mentors who taught it were not at any of the other sessions and therefore didn’t go in with a good sense of where the participants were at. Several people left and went to other room. In future, ensure mentor success by having them loop in better to where the participants are at. Consider next time, encouraging new mentors do a practice session with some friendly folks before they let loose. Also, next session, consider using SeaBorn instead of MatPlotLib.
 
An '''analysis with spreadsheets session''' similar to what we taught last time. This was improved and more effective. By the end, many participants were modifying the code to build their own datasets and doing their own visualizations. One student built a time series of edits to articles about death by police and another to articles about hte NFL. In both cases, real patterns driven by current events became clearly visible.
matplot lib
 
We also ran a session on '''The MatPlotLib session''' which was taught by two mentors we brough in specifically to teach it but who had limited experience with the CDSW. MostSome people in the session were deeply lost. TheBecause the mentors who taught it were not at any of the other sessions, andthey therefore didn’t go in with a good sense of where the participants were at. Several people left and went toIn other room. Inthe future, ensure mentor success by havingwe themshould loop in teachers better to where the participants are at. Consider nextFor timeexample, encouragingwe newmight encourage new mentors do a practice session with some friendly folks before they let loose. Also, next session, consider using SeaBorn instead of MatPlotLib.
- maybe replace it with seaborn?
- tommy will teach it
 
Also, next session, we are going to consider using [https://pypi.python.org/pypi/seaborn/0.1 SeaBorn] instead of MatPlotLib which Tommy seemed excited about.
 
== General Feedback ==
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