Editing Community Data Science Workshops (Fall 2014)/Reflections (section)

== Session 3: Data Analysis and Visualization ==

The goal of the lecture was to walk people through the actual mess of writing 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.

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 sessions ===

We ran two sessions this time.

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 the NFL. In both cases, real patterns driven by current events became clearly visible.

We also ran a session on '''matplotlib''' which was taught by two mentors we brought in specifically to teach it but who had limited experience with the CDSW. Some people in the session were lost. Because the mentors who taught it were not at the other sessions, they therefore didn’t go in with a good sense of where the participants were at. In the future, we should loop in teachers better to where the participants are at. For example, we might encourage new mentors do a practice session with some friendly folks before they let loose.

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.