Report from our inaugural Meetup (with collaboration links)

Thanks to those of you who were able to join our inaugural Data Charrette Meetup! We had a productive first meeting: we discussed the potential dates for both the fall 2018 and spring 2019 events, we discussed software issues, and we discussed some possible nonprofit organizations with whom we could partner for our next event.

Perhaps our two biggest goals, for right now, are these: First, find a way to make the work at the charrette faster by preparing data analysis templates in R and Python and presentation templates in Google Slides; and second, find a way to do at least some data preparation ahead of the actual event.

As for making templates, we have a tiny amount of preliminary work on that in R that was completed by Barton Poulson. You can download these files (incomplete as they are) and other charrette files from our shared Google Drive folder at https://bit.ly/data-charrette-files. (Please contact Bart at bart@datalab.cc if there are any issues with this link or this folder.) The idea is to set up the scripts so that the data can be imported into a data frame called “df” (or maybe a tibble called “tb”) and then split into the outcome variable (called “y”) and the predictor variables/features (called “X”), although it can also be helpful to split those into quantitative and categorical groups (“X_qnt” and “X_cat,” respectively). By using these labels, much of the remaining code can be written generically and easily recycled. (This may be standard practice among programmers, but, as an academic researcher, I only learned about it a few months ago.)

We’ve also created the following web accounts and resources, although there’s not much of anything there right now:

If you need to be granted access, please email bart@datalab.cc. Or if you spot any rookie errors, please do the same!

And with that, I look forward to hearing from you, collaborating online, and – hopefully – seeing you at the next Data Charrette Meetup!

Why non-tech people make data better

On the home page of DataCharrette.org, I mention that our event is unusual in that we specifically designed it to welcome non-technical people in addition to data specialists. There are several reasons for this, but it relates to my favorite explanation of what data scientists do.

In response to the Quora question “What does a data scientist do? Can you become one without being hired as one?” Steve Andrews give this excellent description:

  1. Clean data.
  2. Clean data.
  3. Clean data.
  4. Clean data.
  5. Clean data.
  6. Clean data.
  7. Clean data.
  8. Clean data.
  9. Clean data.
  10. Clean data.
  11. Clean data.
  12. Clean data.
  13. Do some math.
  14. Try to get everyone to understand my findings.
  15. Try to get everyone to understand my findings.
  16. Try to get everyone to understand my findings.
  17. Try to get everyone to understand my findings.
  18. Try to get everyone to understand my findings.
  19. Try to get everyone to understand my findings.
  20. Try to get everyone to understand my findings.
  21. Try to get everyone to understand my findings.
  22. Try to get everyone to understand my findings.
  23. Try to get everyone to understand my findings.
  24. Try to get everyone to understand my findings.
  25. Try to get everyone to understand my findings.
  26. Repeat.

The point of this is that the technical part of data science, in many cases, constitutes only a small fraction of the work involved. Steps 1-12 (“clean data”) can be done by people with even limited technical training (and, at the charrette, it’s usually done in Google Sheets). Step 13, “do some math,” requires technical training. Steps 14-25 (“Try to get everyone to understand my findings”) can often be done marvelously by non-technical people, once they have consulted with the person who did step 13. In fact, it’s often done better by the non-tech people. But, regardless, it makes it clear that there’s room for everyone in the data world, and certainly at the Data Charrette.

Inaugural Meetup to Plan the Fall 2018 Data Charrette!

Meetup Logo

If you’re in Salt Lake City, we’d love to have you join us at the very first meeting of the Data Charrette Meetup, which is a group to help plan and prepare for the charrettes. This inaugural meetup will be held on Wednesday, 11 July 2018, at Impact Hub, 150 S State Street, Salt Lake City, UT 84111. (Click here for a map and directions.)

The invitation is available here. Please feel free to pass this along to anyone you think might be interested and let us know if you’d like to have a Meetup and/or Charrette in your own town!

Announcing the Spring 2017 Charrette!

We’re thrilled to announce that for the spring 2017 Data Charrette, we will be partnering with two outstanding, Salt Lake City nonprofits:

  • Utah Pride Center, which exists to support a thriving LGBTQ+ community in Utah.
  • Spy Hop, a nationally-recognized program for youth media programming.

Thanks to our partner, datalab.cc, everybody who registers for the charrette will receive free access to their video courses on data science, R, SPSS, survey design, and data visualization.

The spring 2017 edition of the Data Charrette will be held in Salt Lake City, Utah, on Friday, April 21, and Saturday, April 22. Thanks to our friends at V School, we’ll be able to share their space at 150 S State Street in beautiful downtown Salt Lake City. (They are located on the third floor of the Impact Hub co-working space.) And thanks to a generous grant from the Office of Engaged Learning at UVU, participation will be free. Lunch, snacks, and drinks will also be provided. And, in a tradition with our earlier charrettes, we will give away two iPad minis, one on each day! This is a bring-your-own-gear event, so make sure you have your laptops and your favorite software. And don’t forget to bring your insight and enthusiasm!

The Details

Location: V School, on the third floor of Impact Hub, 150 S State Street, Salt Lake City, UT 84111. (Click here for a map.)

Days & Times: Friday, 21 April 2017, from 10:00 AM to 5:30 PM, and Saturday, 22 April 2016, from 10:00 AM to 4:00 PM. You can come one day or both days, and you come and go as you please. We’re happy to have you there in any capacity.

Food: Free lunch, snacks, and drinks, thanks to UVU and V School!

The Ukrainian Data Egg: To Be Revealed!

We have some excellent swag for the Utah Data Dive but perhaps the most interesting will be a custom, handmade, data-themed Ukrainian Easter Egg by Lehi artist Rynna Poulson (here she is on Etsy). We figure it will be a simple variation on all those other Ukrainian data eggs… oh, wait, it’s a category of one. This will be available as a raffle prize on Saturday afternoon – as it is still being created – but it should be wonderful. Photos will be posted upon arrival.

While we’re at it, the Utah Data Dive is getting a standard chicken egg, but Rynna has offered to create a Ukrainian Big Data Egg… on an ostrich egg, of course. Contact Rynna via her Etsy page, Ukrainian Eggs Etc. A must have for any serious data egg-head! (You knew that was coming.)

The Democratization of Distraction: The Utah Data Dive Collaborative Playlist

You too can contribute to people not quite being able to pay attention to the data at hand by contributing the The Utah Data Dive Collaborative Playlist on Spotify. Music that is more or less maybe related to something data-like is welcome, as well as anything that helps you get your data on.

Official Utah Data Dive Theme Song

In lieu of doing something truly useful for the upcoming Utah Data Dive – although I did get the tables, power, and wifi arranged this morning, which is good – I have chosen an official Theme Song for the event. I’m not actually going to tell you what it is but I will let you know that it’s in a one-song playlist on Spotify and you can get to it here. (I believe you have to have a Spotify account to hear it, but I really don’t know how these things work. And, to head off any complaints about Spotify exploiting artists, I will mention that I have FIVE paid accounts to Spotify, so I’ve paid my part. You should, too.)

By the way, I’m also in the process of creating a public playlist for data dive songs that other Spotify users can add to. Get your geek on!

Enjoy and I’ll see you this weekend!

Addendum

The beat to the still-incognito Official Utah Data Dive Theme Song was liberally borrowed from its creators and made popular in the US by Afrika Bambaataa in his song “Planet Rock.” This poster by Rob Ricketts is a depiction of the programming for the Roland TR-808 drum machine that creates the beat. It’s also an excellent visualization of musical data.

Planet Rock - Afrika Bambaataa - 808 Pattern 1

More food, more swag

I felt very optimistic when I decided that maybe 50 people would come to the Utah Data Dive, so that’s what I wrote the grant for. But now we’re still over two weeks away and we have nearly 60 people registered, with perhaps another 40 who will show up without registering. As a result, we have had to do some financial gymnastics but we’re going to make sure to have food and t-shirts for 100 people! (And silly raffle prizes!) So don’t hesitate to come down!