Mar 28 2016

Schmoozing with Portland Data Scientists

Here are the topics that Portland R Users say they are interested in:
r_topics
I’m interested in those topics, too.  And the several other data science MeetUps have similar topic profiles.  But when people ask to join the Portland Data Science MeetUp, for example, they say they are seeking things like:
  • Networking.
  • Meet people with similar interests.
  • Better sense of the Portland data science community.
  • Meet more people in the community, and learn what types of data work goes on in Portland.
  • Meet other people with similar interests.
  • Meet colleagues and hear about their best practices, projects and approaches to solving problems in the data science space.

percepronmeetupThat’s what I want, too.  But most of the Portland data science MeetUps seem to consist of sitting in front of a speaker who’s in front of a screen talking to a group of people who are looking at their computers.  Not that much chatting with the person sitting next to you.  How can a local, mainly face-to-face group find a useful function in a larger learning ecosystem that includes (for R, at least) Twitter, Stack Overflow, R-Bloggers, various mailing lists, etc., etc.?

Some of the event interaction behavior that I’m seeing is venue-related, where the room layout and seating encourages limited cross-talk and mostly passive participation.  But what the MeetUps platform itself provides is somewhat lacking as a community platform.  It has some opportunities for discussion and interaction online, but postings from members seem to be mainly about what an informative and interesting presentation that last sessions was.

Stepping back to look at the Portland’s Meetup scene more broadly (all my R code for data retrieval is on Github and a vignette is here) shows that there are lots of them and they come in all flavors.

Meetup-group-membership-by-category

By far the biggest groups are in the “Outdoors”, “Social” and “Singles” categories.  “Support”, “Moms and Dads”, and “Fashion” are the smallest groups.  Obviously most of those groups are not sitting looking at computer screens when they meet.  But as a whole MeetUp groups make for a fascinating community laboratory. It makes me wonder what reasons are there for a group to grow very large or for it to stay small and differentiate from other similar groups?  Here’s a look at 5 groups in the data science area as they’ve grown over time:

meetup-events

The Python MeetUp group is big for several reasons:

  • it’s the oldest,
  • the language is used for data science purposes as well as for programming more generally
  • it has a mix of large and small meetings (based on the number of RSVPs; R Users and the data science groups have a similar mix),
  • it has had  regular meetings with consistently large RSVP numbers,
  • no interruptions (like the R group)

data-science-meetup-leadersWhat’s going on here?  Although I’ve found that go out for drink afterward is exactly where networking (and a lot of the learning) go on. To find out, I got involved with a small group that was working to bring the several data science MeetUps closer together, since there is a lot of overlap in the topics they cover.  We’ve met in bars and coffee shops to talk about a federation of MeetUps.  Of course during our meetings everyone had to stare at the computer (including me, but my community background compelled me to step back and take a photo of the group).  In the photo most everyone is looking at a Google Doc where we are writing a collective document about how to move our several MeetUps forward individually and together.

One strategy that we came up with was to set up a Slack Team room where we would expect more chatting could take place, even during a meeting.  However, to create a way for MeetUp Group members to join a Slack team space involved two other platforms: Google Docs to do our planning and Github to create a common website for the federation of MeetUp Groups.

pdxdata-tool-pathway

Here is a re-cap of the functions and issues that I see in the use of these four platforms.

Meetup.com is oriented toward “getting together”. It has good group discovery, an easy way to affiliate with (or join) a group, good meeting notification, a nice way for members to link to their Twitter and LinkedIn pages, an RSVP function that allows for meeting organizers to deal with smaller venues, some linking with other members, and a funny “attendance” function (where you click on a “good to see you” link, in effect indicating who actually showed up at a meeting).  It has some features that limit a community’s interactions, including an orientation toward “the next event” rather than a topic orientation (i.e., “what we know” or “what we have learned”).  MeetUp has a limit on the number of characters in a comment, so meeting notes can’t be very long at all. It also shields member identity carefully by making it difficult to share your email address through its individual messaging channel; in effect it tries to keep you tied to Meetup for member-to-member communication.

We decided we wanted to add Slack.com as a data science federation platform because it’s oriented toward “being together” (or at least “hanging together”, or at least “chatting together”).  It makes it easy to have multiple chat channels, has good (easy to control) notifications, makes it easy to drag & drop documents and files into a channel, has excellent search within a team space, feels “private”, and supports closed groups within a larger team structure.  It also works nicely on a smart phone as well as on the web.  In addition to the fact that a Slack team room is not being discoverable via a search, a it requires users to be “invited,” which could have become a labor-intensive job for a loose group like us.

We found ourselves using Google Docs to discuss and plan how to “federate” the several data science MeetUps, because Google Docs are oriented toward “thinking together”.  Being able to share documents, control who can write to a document, and have multiple people write in a document are all very useful functions. Although Google Docs work well for a small leadership group, they aren’t so effective for communication within a very large group, partly because of the very document structure.

Although github.com is basically a  “coding together” platform, it also turns out to be a very social platform. Github pages was the easiest way to set up our data science federation website: http://pdxdata.org/.  We were able to borrow a trick from The Ann Arbor User Group for automating the Slack Team invitation process.  Github is quite social for its limited technical user base (a STAT 545 class at UBC even uses it for class Discussion).

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