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

== Participants ==

We had 30 mentors who attended at least one of the sessions and at least 20 mentors at each sessions. Many of our mentors were UW students in more technical departments like [https://www.cs.washington.edu/ Computer Science and Engineering] and [https://www.hcde.washington.edu Human Centered Design & Engineering]. Perhaps half of them worked outside of the university as software developers.

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 affiliations. Affiliations listed by at least three people include the following:

{| class=wikitable
! Department !! Participants
|-
| HCDE || 16
|-
| iSchool || 10
|-
| Communication || 8
|- 
| Anthropology || 3
|- 
| Alumni || 4
|- 
| Undergrad || 3
|- 
|}

We had two people each who listed their affiliations as Bio- and Health Informatics, the Foster School of Management, Microsoft, and Wikipedia. 

We also had people from Psychology, 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.

Retention between session and 0 and 1 was nearly 100%. Retention between sessions 1 and 2 and sessions 2 and 3 was roughly 75% leaving us with perhaps 55-60% retention between session 0 and session 3.

Anecdotally, there is a sense that those who are dropping were those who had trouble but who didn’t struggle visibly. 

Although our participant pool in [[CDSW (Spring 2014)]] was overwhelming female (80-90%), there was close to gender balance in both students and mentors this time around.

Once again, quite a large number of people applied were already skilled programmers. We're still not exactly sure why these people are applying because we think that the fact that the workshops are for absolute beginners is very clear. Perhaps people just want more exposure to data science?

Once again, the constraint on scaling the workshop was the number of mentors. Every mentor we added means that the workshop can accommodate four more participants.

One suggestion was allowing participants with have some programming skills — especially for the second and third workshops (given predictable rates of retention). There was not consensus among the organizers and mentors on this approach and preferred getting more newbies and invest more in them?