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

We had about 150 participants apply to attend the sessions. We selected on programming skill (to ensure that all attendees were complete beginners), enthusiasm, and randomly to maintain a learner to mentor ratio of between 4 and 5. We admitted 80 participants. 58 listed a UW affliationsaffiliations. Affiliations listed by at least three people include the following:

We also had people from PyschologyPsychology, the City of Seattle, the Low Income Housing Project, Seattle Meshnet, Biochemical Engineering, Bio Physical, Chemical Engineering, Game Studies, Linguistic, College of the Environment, Oceanography, the School and Public Health, UW Bothell, Central Washington University, and many people who did not specify an affiliation. We continue to think that it's important that people who are not doing research but who are are part of online communities were in the mix with UW-type researchers. Bringing together researchers and participants in online communities is an important goal and would like to work toward more balance in this regard and to increase the amount of non-UW participation.

Our biggest challenge with growing the workshops was with physical space for the lectures. Basically, rooms thathat can hold more than 100 people at UW are almost exclusively lectures halls that make it almost impossible for mentors to physically reach students in order to help them debug and solve problems.

We reserved a lecture hall that fit 200 people and filled it with 100 students in alternating rows to make it at least possible to reach each person. This worked reasonably well althoughtalthough it was still suboptimalssuboptimal.

The goal of this session was to get users setup with Python and starting to learn some Python basics. We changed the curriculum originally used by BPW enormously to use Continuum's Anaconda instead of Python directly from [http://python.org python.org]. The result was staggering. Not a ''single person'' reported "many problems with set-up" (i.e., respondantsrespondents reported either "no problems" or a "few problems.")

* Anaconda seems to have at least some remaining i10n bugs. For example, one studdentstudent had a home directory set to a Chinese string which caused the Anaconda installation to fail at a late stage. This was eventually fixed by a mentor who changed the path by hand.

* We need to do a better job of modellingmodeling sticky notes so folks use them more effectively.

* We want to seriously investigate the possibility of moving to Python 3 to try to address lingering unicodeUnicode issues.

* Do a better job of briningbringing folks back to gethertogether to walk through potential solutions to the questions posed in the project rooms.

The [[Community Data Science Workshops (Fall 2014)/Day 2 lecture|morning lecture]] was given by Frances Hocutt and it was well received. Unsurprisingly, the example of [http://placekitten.com/ PlaceKitten] as an API was an enormous hit: informative ''and'' cute.

Frances used excellent slides which are shared [[Community Data Science Workshops (Fall 2014)/Day 2 lecture|on the wiki page]] and which we will reuse. About half found Frances’sthe lecture either too fast or too slow and about half found the lecture to be just right.

There were three parallel afternoon sessions on '''Twitter''', '''Wikipedia API''' and '''SQL'''. All three werewwere successful and we plan to do some version of all three sessions next round:

* TweePy continues to be both poorly documented and opaque. TtheThe opaqueness of tweepyTweePy was a problem and we may want to create an interface to TweepyTweePy that just gives users raw JSON.

Jonathan ran a session on using SQL. Although this was a diversion from the strong Python focus, it was well attended and apprecaitedappreciated by students tryinttrying to build up this skill.

* Generally thjethe session was was very successful and seemed to do a good job of giving people an overview of a data science and a way to hook themselves in to it.

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

We also ran a session on '''MatPlotLibmatplotlib''' which was taught by two mentors we broughbrought 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 MatPlotLibmatplotlib which Tommy seemed excited about.

* Generally, there was a sense that we should stop creating pages in the wiki by copying and pasting old stuff. This was the BPW model but it's leading to madness. We when archive an old version of a site, we can use MediaWiki to create links to the old version of the pages (we can intstallinstall templates from English Wikipedia to help make this easier).

* There was general interest in pair programming or more team based excercisesexercised. We should consider changes along this line.

Once again, we had certain afternoon project sessions that were much more effective than others. One thing we were conflitedconflicted about was whether we wanted more break-out sessions or whether we should just use the best of the break-out sessions (perhaps in two rooms).

* PrecannedPre-canned sessions make it easier for new mentors to feel confident and be successful.

We should pursue other ways to encourage creativity with code. For example, we might give participants creative/flexible moments within sessions and lectures might be empowering in simlar ways. We can also continue to call out participants who are doing creative things.