Wednesday, March 27, 2013
ETA: Web video of my talk is here on the Pydata vimeo site.
My talk was generally well-received, although I think I flummoxed the stats graphics people a little bit who probably weren't expecting something so "sketchy" from me. Hey, I love those other tools too, and use Matplotlib (and d3 too!) regularly.
A few quick comments on the Nodebox eco-system: The current focus of the team in Leuven is on Nodebox 3, a block-diagram visual programming tool, not the 2 variants I talked about (Nodebox 1 and Nodebox OpenGL). I think NB3 veers away from usefulness for the data science crowd that might benefit from a Python alternative to Processing. If the enormous success of the java-based Processing is anything to go by, I'm not crazy in thinking a Python tool like it should be huge! After all, it's cuddly Python! So at the end of my talk, someone actually asked me why he should have sat there for 45 minutes if I was not talking about thriving open source code with a huge community behind it. My response was, more or less, "It's already super useful which I hope I showed, and more people could be working on it than just the original authors." That's how open source works, right? (By the way: That guy apologized to me later, but I didn't take it badly when he said it.)
A couple more comments on my slides: My own data experiments in the deck weren't incredibly successful, largely due to issues with the database I used. I wanted to explore Shane Bergsma's gender-of-nouns database collected off Google news, and what I found was that it thinks everything is really "male." Cuz most news articles are about men, probably. (Also, it proved less useful on older Gutenberg books, because old-fashioned vernacular nouns don't appear in the db, like "momma." So out went Pride and Prejudice and out came my credit card for Kindle books.) Hence, all my fiction gender plots look kind of like these, with heavy weights towards male and neutral nouns:
The pdf of my slides is here and the code zip file is here. Do check my appendices: I figured out a bunch of issues related to paths in Nodebox 1, running NB 1 from the command line, and the like.
A couple nice post-conference mentions: Jake Vanderplas's take on Matplotlib history and visualization in Python, which has some interesting comments. I spent a while talking to Ben Lorica (@bigdata) at PyData, and he nicely mentioned Nodebox in his well-RT'ed article on how Python Data Tools Just Keep Getting Better.
Also, before the conference, I was interviewed for a podcast about data vis skills. I didn't advertise this very broadly because of a few mistakes in the initial post (one in particular that claimed I hated d3, which is certainly not true at all -- I said it had a learning curve, you can listen yourself!).
Friday, February 15, 2013
PyData SV 2013 in March
Peter Wang from Continuum.io asked if I'd submit something to PyData SV, perhaps after I noted the lack of women speakers at the last 2 events. :-) This small conference is the best place for python data science talks -- I've enjoyed and learned a lot at both previous ones. I'm happy to be talking about using the Pythonic versions of Nodebox as tools for data visualization.
Lean UX NYC in April
In April, thanks to Will Evans, I'll be giving a workshop on quantitative skills and analytics for product designers at Lean UX NYC. Here's an interview with me on their website, talking about becoming quantitative and lean data organizations. I'm still toying with the final content, but I expect to cover some advanced Excel maneuvers, a little bit of Google Analytics analysis, and some stats of use in UX work.
OpenVis Conf in May
It's a new visualization and data conference, the OpenVis Conf! Bocoup.com and @ireneros are running a great new event in Boston, and I'll be speaking too! Here's my talk plan (titled "The Bones of a Bestseller"):
How do Dan Brown and Stephanie Meyer do it? Most text visualization focuses on word counts: in this talk, Lynn will illuminate how fiction "looks" at a meta level, using a combination of meta-linguistic analysis and simple machine learning. Beyond just words, long texts are composed of sentences, paragraphs, and chapters, and the pacing and theme are reflected in these as well as word choice. With a little finesse, we can detect and graph the famous story arcs that screenwriters and fiction teachers are always talking about. With a little more finesse, we can write an action scene detector or a sex scene spotter and visualize how exciting a novel is — in all senses.
I know a bunch of Twitter friends are coming to all 3 of these conferences... I can't wait to see you all!
Sunday, November 04, 2012
A week ago, I gave a talk at Strata NYC on network visualization ("Beyond the Hairball"). The talk had many technical issues (I'm new to using a MBP and Keynote to present), but the slides seem to have had some kind of life on Twitter. So here's the rather large and slightly academic deck:
I was gratified to get so many RT's, email, and favorites from people including Gilad Lotan, Steven Strogatz, and Ben Shneiderman.
Strata itself baffled me a little due to size and "big data" hype factor -- I got a little tired of overhearing businessmen on their phones talking about "monetizing social." (Why did "social" have to become a despicable noun?) My favorite moments were certainly social more than technical: getting to meet Noah Iliinsky and Kim Rees, seeing Danyel Fisher from MSR and his game analyst partner Kim Stedman, Wes McKinney (with his new book, Python for Data Analysis), and Jon Peltier and Naomi Robbins. These folks made for a very nice data vis and python slice of the big data conference.
Then There Was PyData!
I love it when a technical conference isn't afraid to show code, and make code available. That was PyData! Here were some highlights for me (with two tracks, I missed half of it!):
- Timeseries in Pandas, from Chang She
- NLTK, or "Just Enough NLP with Python" from Andrew Montalenti (See also his "Webcrawling and Metadata" slides)
- Statsmodels and Patsy from Skipper Seabold (his 538 model in python is here)
- The always wonderful scikit-learn tutorials from Jake VanderPlas (here's a homepage for some of it) and Stefan van der Walt's mind-blowing scikit-image stuff
- Brian Granger's really excellent overview of updates in the IPython notebook (here's a general tour of the notebook in the online notebook viewer; and here's their example notebooks folder on github)
All in all, Pydata was a good couple of days, well worth the trip! They could stand to get a few women to speak at the next event, though. (No, I'm not volunteering!)
Saturday, August 18, 2012
I grabbed it and had a poke around. My curiosity was less about EL James (although mad props to a fanfic writer for making the big time) than about general genre and gender distributions. At a base line, when I did some hasty labeling, I saw that as expected, fiction overwhelms, and women are writing more of it:
Then I threw in some data analysis, just to see if there were any trends there. Okay, EL James finally pops up. Maybe there is a mild trend towards women selling more here?
Then I looked at publishing houses. I wondered if any of them were perhaps making more money off women than off men, and which ones?
This one suprised me a little more than I expected. Bloomsbury publishes JK Rowling, of course (and Khaled Hosseini, who incidentally lives in the USA). The rather macho-looking Transworld has Dan Brown, Bill Bryson, Richard Dawkins (and also Joanne Harris with Chocolat).
But the quickie plot that got me really motivated to spend my Saturday on graphs was this one:
My first thought was "How very irritating: the women bestsellers are all labelled as writing for children, even though they dominate the list. And what is up with the science fiction and fantasy group there?" It seems that one of JK Rowling's oeuvre was a top seller in its "adult" edition too. The one male author in SF&F is Tolkien (for Lord of the Rings, all of them, I suppose). Which means that no actual "science fiction" is on this list, it's all fantasy, if you're tracking genre like I do. Other big names for kids are the Twilight series and Hunger Games series, also stuffed into "Young Adult." EL James is classified in "Romance & Sagas." I guess there's no "Porn" category, or "Adult," like there is for movies, which I think is a real prudish shame.
I regrouped a bit; I put the fantasy books together, whether they are for "kids" or not. That includes Philip Pullman, Stephanie Meyer, Suzanne Collins. Most adult women I know have read JK Rowling, Stephanie Meyer, and Suzanne Collins. I actually find it disgusting that publishers would trivialize these authors as writing "for kids," especially given what's in them -- but that's a gender genre rant for another day. I left Time Traveler's Wife in General Fic, although it could go in Romance or Fantasy, I suppose. I put the two Bridget Jones in with Romance, although I feel the Romance vs. General Fic to be a rather slippery slope. I did not put Chocolat in Romance. I grouped the Biography and Autobiography together. The food and diet related items seemed most interesting as a meta-group. Here's my remash of the genre and gender stats:
Men are represented in more genres, even with my regrouping. But why are there no women in Crime, Thrillers and Adventure on this list? Where are the women mystery writers? (Did I perhaps miss one I should have categorized as genre?) Likewise, there are no male Romance & Saga writers shown here. Yeah, I think the jokes about male "sagas" really are due at this point. (Note a couple links from a recent Twitter exchange on long books by men: "The Exasperating Maleness of Long Novels" and "Why Don't Women Write Long Novels?".)
Finally, I did one arranged by author, just to see, and of course JK Rowling rules the list. Male and female authors are pretty evenly distributed throughout, as well.
What's most interesting here is that the very last one listed is Suzanne Collins, despite the recent Amazon announcement that the Hunger Games books have now outsold Harry Potter on their site. Curious! A US/UK difference? Ebooks not accounted for in the Neilsen data?
Just in time, the Hunger Games DVD is out, and I know what I'm watching tonight. It's also high time I gave Fifty Shades of Grey a shot, even if it's not SF & Fantasy. If you want to check my recodes and the original data, I uploaded the spreadsheet with my new columns here. Please let me know if you think I made any mistakes in recategorizing (or especially gender labels).
PS. I screened out the weird Beano entry, which has no author listed. So this is really about the top 99 books.
PPS. At Readercon recently, a bunch of SF&F writers on a panel said the way to publishing success was to "write a boring thriller." (Me: "I could totally do that!") Now I think it's: Write a great fantasy with teen heros that a publisher will buy from a woman, that in a great act of resistance against age-ist stereotyping by The Man, adult women everywhere download and enjoy shamelessly and tell each other about where male publishers can't hear.
Wednesday, July 04, 2012
What could be more appropriate for July 4th than Captain America?
If you've seen the movies a lot already, and you're wanting more, there's always the fan fiction. Of which there is a lot. I admit, I read it. And I got a little data curious over the weekend.* WARNING: Look away now if the idea of hot boy-on-boy superhero action makes you queasy, because there's a lot of such hotness in the fiction. (Also hot girl-on-girl, and girl-on-boy, and girl-on-boy-on-girl, and god-on-HULK-on-brother, etc, cuz it's ALL there.)
Surprisingly, the dreamy super-soldier Captain Rogers is not getting quite as much action as Tony Stark is. And there are a few other surprises in there, if you click around on this little chart.
Yes, that's right, even DEAD people are getting it on in these stories. Try clicking on Phil Coulson. If you were (like me) blind to suits over spandex, he's "Agent Phil" with the tie. His favorite bunk buddy is "Hawkeye" Clint Barton! I wasn't sure they ever even talked to each other until I rewatched Thor last night (Coulson stops Clint from shooting Thor when he #fails with the hammer in the plastic building; they seemed to be on a purely last-name basis, but what do I know!).
Thor and Loki seem to have it hot and heavy too, family issues aside -- hey, they're gods, they both make and break the rules.
I'm personally a little disappointed not to see more girls getting action here, but I am definitely down with the allure of Tony Stark and Steve Rogers. All those muscles, all that antagonism to overcome! But there is surprisingly little lesbian romance in the archive. (Hang on, is there a "no two red heads" rule? Aren't they both red heads?)
But back to Tony and Steve... I dug a little further and discovered that their sexy love stories were pouring in well before the movie, as early as 2008. With the release of the movie, some new pairs got steamy, like Tony and Bruce, who really were so adorable playing with radiation together and poking each other (snicker). And there must be something in the comics about Phil Coulson and Clint Barton? It might get expensive to look into it. Perhaps I need to visit Jer Thorp('s comics collection).
But let's go back to Tony (again). He gets all the action, even if he doesn't wear spandex and look like a Norse god. And despite that unfortunate facial hair! Captain America has no serious contender for SO apart from Stark. Tony's a reformed weapons dealer, "genius, playboy, billionaire, philanthropist," who engineered his own superheroness, without any magic, medicine, or lab incidents. It's no surprise to me that men AND women love him, and he gets the bulk of the fan fiction. He's on top of the world, so of course he's on top of Steve Rogers! Tony Stark is a self-made superhero, and that's why he gets laid the most. In one of the best Tony lines ever, scifigrl47 has him saying:
"I somehow managed to get CAPTAIN AMERICA doing the horizontal mambo. Fuck you all, I win. I win everything."
And that's pretty much a real American hero talking.
Friday, June 15, 2012
As an ex-researchy type, I'm used to the papers and speakers at conferences like Infovis, the academic visualization conference that meets during IEEE Visweek; but last week's Eyeo Festival was... different. In the past few years, I've been to a handful of the former-Flash-community's digital art conferences (such as Geeky By Nature and Flashbelt). They inspired me, made me think about the value of personal digital art projects; but as someone who wants to work in data visualization, Eyeo was more challenging to me. In a good way!
Who Was There
The audience was itself pretty amazing - you could tell by the Ignite-style talks on the first evening, which blew me away, including pal Jen Lowe (@datatelling)'s talk on the human in the data deluge, feisty Rachel Binx on animated gifs, Sarah Slobin (@sarahslo) from the Wall Street Journal, CSS artist Val Head (@vlh), Bryan Connor of The Why Axis, Sha Hwang's (@shashashasha) dry awesomeness... The non-speaking audience turned out to be pretty astounding as well, including Jesse Thomas of JESS3, Jeff Clark of Neoformix, Mike Bostock (@mbostock) who created D3.js, JanWillem Tulp, some dude from a strategy firm advising the British Government on technology, folks from MOMA and the NYPL and the Met, and, well, really pretty much everyone I talked to was intellectually interesting in some compelling way.
The chance of having a randomly interesting conversation was extremely high -- for example, it turned out that a guy I got to chatting with as we crossed the street lives in my area and had been intending to email me about his startup after hearing about a talk I did locally.
Who Wasn't There
There were not many people I associate with the academic "infovis" scene, and a couple of us wondered about that. Likewise, at the Infovis conference last year, the data artists and vis consultants of Eyeo were not present either, see my post moaning about that here. I put it down to a handful of things: the Eurovis conference was the same week (super awkward if you wanted to follow the hashtag and had bad wifi/phone as I did), and the tickets to Eyeo sold out in less than a day, so if you weren't paying attention, you weren't in that audience.
Since Visweek this year wants to get practitioners in the mix, I think there are some things the organizers might learn from Eyeo, given how much data visualization was there: are you spending time understanding what practitioners are inspired by, what tools they use, what they're trying to learn, what they want to work on, who they want to talk to or get advice from? Academic conferences result in papers that can be read, which hopefully contributes to the evolution of the discipline (if they are accessible afterwards -- Eurovis papers do not seem to be!), but this means it's easy to justify not attending in person if you're a practitioner. A conference must have value outside the papers for the non-academics, like provocative panels, tutorials for skill building, networking options with a great audience, drinking... otherwise, we can all just read it later.
Eyeo was definitely a conference to be AT -- not to escape to go hack in your room during sessions, and not to read about later. Watching the recorded videos will give you a flavor, but it's not a replacement for the serendipitous goodness.
The Eyeo speakers who "overlap" these two communities the most in content seemed to be Amanda Cox of the NYT Graphics team, Fernanda Viegas and Martin Wattenberg, and Moritz Stefaner. Amanda Cox was capstone presenter at Visweek last year, and was a highlight of that conference for me, because of how much she made the newspaper vis problem a design problem, even confronted with a lot of data in R. (Amateur tip: Don't tell her you have a crush on her, it won't end well.)
These folks bridged the two conferences a little bit, but the gap still feels overly large to me. I hope to see more discussion of artistic design aesthetics and process at Infovis one of these years!
More On How the Sausage Was Made
A deeply valuable aspect of many talks was that superstars showed us how the sausage was made, with really funny commentary about the mistakes along the way. This is something I rarely get out of academic conferences, which would help me learn and would help my morale, I have to admit.
Martin and Fernanda actually showed their Java project in eclipse, used to sketch the pre-final wind flow map. Their intermediate stages were shockingly terrible ("look away if you're epileptic"), until they stumbled on the right direction with gradient arrows. So reassuring to see the guts and thought process re-enacted! The final solution is brilliant in its simplicity, but it took a while to get there.
|Wind map detail (Viegas and Wattenberg)|
Felton's process for his annual report design was wonderfully self-deprecating and revealing: 15 days of a mostly blank page, feeling "like a mouse in a bathtub" with no traction, until he started making structural decisions for the framing of the latest annual report. And watching him edit his slides over his shoulder before his talk was an education in itself (he uses InDesign and Illustrator).
Moritz Stefaner showed his designs for the recent meusli project; he chose the chord diagram over the possibly-more revealing matrix design because the matrix doesn't look "tasty" and "meusli shouldn't look like fungi."
|Stefaner's rejected "fungi" visual of muesli|
Moritz was also very thoughtful in his explanation of why they deliberately avoided axes labels and the addition of the purely ornamental role of the particles in the Max Planck Research Networks.
A slightly random aside about one of Moritz's projects with Nand.io -- but if like me you wondered what that floating mill on the River Tyne looks like:
|Tyne Floating Mill (Source of stats for the beautiful Tyne Flowmill visualization)|
And here's a snap from the beautiful Tyne Flowmill project image archive:
|Tyne Flowmill visualization details|
You can find this same vein of honesty about process (and failures on the way) in the excellent and often funny Chartsnthings blog of the NYT Graphics team process, run by Kevin Quealey (@KevinQ). Kevin was there too and took the news of my crush on Amanda much better than she did. I was told that the NYT has no budget for travel -- yet there was a sizable contingent from their Research Labs and the Graphics Team. I guess that's the best indicator of a conference that's a successful destination event: People will pay their own way to attend.
Repeating yet again, I got really interested in that process stuff. So, here are some of the sketching/intermediate tools used before the final versions that I heard or inferred:
- Hand-written calculations and paper highlights (Stefanie Posavec)
- Java (Martin Wattenberg and Fernanda Viegas)
- Processing and MySQL, pdf exported into Illustrator and InDesign (Felton)
- Tableau and Excel and Gephi and Processing and Photoshop (Moritz Stefaner)
- R (Amanda Cox) [I know this from the Chartsnthings blog and a previous talk; although the NYT Graphics team uses many other visual design and development tools as a group]
- Processing (Ben Fry, Wes Grubbs, Jer Thorp)
- Dat.GUI (Koblin)
Did I miss any, anyone know?
Worthwhile Career Risks
It's risky enough to be an independent consultant these days without sophisticated insurance, but it's even riskier to try to do artistic information visualization that is stays honest and solidly grounded in the data -- there are only so many gigs with Wired or GE or Popular Science out there. The people I admire seem to have them pretty well covered. Getting a business informatics gig is a lot easier, given the millions of startups and companies rolling in data right now.
Even more risky is trying to limit your work to visualizing "data for good," the motto of Periscopic (represented at the conference by Kim Rees (@krees) and Dino Citraro), echoed by Jake Porway with his new DataKind project and Code for America, represented by the very articulate Jennifer Pahlka. As Jer Thorp asked of the panel he organized, "How do you do this kind of socially conscious work, and still pay rent in New York?"
Luckily we're not all in NYC, but the same question holds in some form for most independents: turning down work based on ethical questions about the client or the data message is a luxury not all of us can afford. When "brands" come knocking, it's tough to say no, especially if they're rich and you're not. Jake, at least, suggested a path to being involved as a part-time volunteer (see his "I'm a data scientist" signup page). Incidentally, Porway's example of a visualization that "does good" was the animated timeline that illustrated the spread of London Riot rumors--and their corrections--from the Guardian:
|London Riot Rumors on Twitter from the Guardian|
I found this theme of "doing good with data" an inspiring reminder, and I'm glad to see it playing a part in this kind of high-touch, high-concept conference. Enormous thanks to the organizers for this, and to the Ford Foundation sponsorship for enabling them.
Insecurity Admitted, Hilariously (and Inspiringly)
Why were so many of the speakers so funny? No one is funny at academic conferences, unless they've had a lot to drink and you're no threat to their tenure process. Robert Hodgin (@flight404) is basically an artistic standup comedian. Or a comedian who does digital art, I'm not sure. And he's like that every time I see him, with whatever material, so it's not like this was super-practiced.
I almost hurt myself laughing when Jer Thorp described some of the weirder Avengers from his massive comic collection (Whizzer, Starfox who "stimulates the pleasure centers of the brain" and was therefore brought up on charges for sexual harassment at one point?!).
And you too can enjoy Ben Fry's recap of the critic who accused him of having a Degree in Useless Plots from Superficial Analysis School in his "I Think Somebody Needs a Hug" post. But I'm still waiting for the post of the very funny Famous Writers drinking saga by Ben's colleague at Fathom.
There was a lot of wry humility, too -- Wes Grubbs recounted how proud they were of the spread of their Wired piece for 311 calls, and then found out that it was in an issue with cleavage on the front. Regardless, Tim O'Reilly used it in a TedX talk and it got a slot in the MOMA Talk To Me exhibit, so I think it had legs even without the cover boobs.
|Pitch Interactive for Wired|
Speaking of humility, a few days after the conference, Robert Hodgin posted his astounding talk code on Github with an awesome, hilarious, apologetic README that should be required reading for anyone trying to learn creative coding without a computer science degree:
I recognize that I can make some great looking work, and I am proud of this fact. But as soon as I am engaged in a code-related conversation with someone who knows C++, someone who knows proper code design, someone who knows how to explain the difference between a pointer and a reference, someone who polymorphs without hesitation, the bloom falls from the rose and I end up looking like an idiot. Or even worse, a fraud.
If Robert Hodgin and Jer Thorp can feel like hacks or frauds, then maybe it's okay if I have imposter syndrome too; and if I want to make an interactive vis project of Avengers' penis sizes for charity, I should probably just own that desire and run with it. And if you made it this far and now you don't want to hire me for anything, so be it! Everyone needs to pursue their dreams.
Sunday, May 13, 2012
I thought I'd contribute one story to the "telling stories with data" genre, even if it's a silly one. It's silly 'cuz it features such a silly graph, which I shoved into an appendix of a presentation for a client a few years ago. Here's an anonymized version:
I put that animation with the arrow in there on purpose, because when I presented it, I had to point out the skinny line on the top. More graphs than you'd expect come with a "performance" part and in some contexts, I think this is just fine. Afterwards, one exec at the company referred to it often as "that chart with the one pixel line." (Okay, technically it had about 2 or 3 pixels. Not as punchy if you refer to it as "that chart with the 3 pixel line" or "that chart with the thin red line.")
I'm sure there are other, better, ways to present this red-and-orange tower. The point is: It was remembered. It had an impact. This graph led to more graphs being created! Roughly, we saw these steps:
- Acknowledged and admitted: The one pixel red line was considered to be a problem (or rather, the un-analyzed orange bar was).
- More descriptive graphs were made: This is key &emdash; an influential graph/chart always leads to more data investigation, with more graphs. Describe the size of the problem, delve further. The giant orange segment was tackled: How could it be made manageable? What patterns existed inside it?
- Sensemaking/iterpretation: What could we do, what couldn't we do? What should we prioritize or safely ignore? What tools were needed? Who owned what parts of this orange bar?
- Data tools sprouted: A series of ad hoc and then longer term tools were built: Excel reports with perl/python/VBA, then a Flex tool for intermediate data dives, then a dashboard in Flex for tracking larger picture trends.
Do It Well, and Do It "In-House"
It's an old analytics saw that you can't improve what you don't measure. Well, I think you won't improve what you don't measure meaningfully and then pay attention to. The client had collected the data, but then did nothing with it, because no one had made understanding it a priority. Data for data's sake is pointless and will be ignored. At the time of my one-pixel bar, an analytics cheerleader in the company described our primary data system as "buggy, opaque, brittle, esoteric, confusing." I'd add, "understaffed," and as a result of all that, usually ignored, which is how the one pixel red line came to be.
We took a brief detour in which we considered "outsourcing" the data problem to another company to do the top-level reporting for us, but our (mostly my) investigations suggested we couldn't do the fine-grained, raw-to-dashboard (ETL) reporting and analysis we needed without owning the entire pipeline ourselves. Because in all these organizational, data-driven settings, the reasoning goes like this:
- What's going on? Now, and as a result of previous behaviors/changes. Do we have the right data? Trends, alerts, important KPIs.
- Why is that going on? Drill in. Question if we have the right data and instruments to diagnose. A deep dive occurs, often all the way back to RAW data. This is normal! And this is necessary.
- How might we change the bad things? This is a complicated question, never simple and often not just quantitative. This is where the profound thinking happens, when the cross-disciplinary methods and teams pull together to interpret and chop data. Sense-making and interpretation require lots of checks on data, reasoning, and context.
Our ultimate data team was a cross-company, somewhat ad hoc group of people who cared about the same thing, but didn't report together anywhere: Customer Support, UI development management, directors of development and the API team, a couple of database gurus. Oh yeah, let's not forget the database gurus: I couldn't have even made that bar chart without badgering the database guys for info on their tables, so I could do some SQL on it.
In a year, we had achieved measurable significant improvements, via that cross-disciplinary team, and without out-sourcing our important data in any way. The short-term tools paid off almost immediately, and I hope the long-term ones are still evolving. One of the team members won an award for the tool he developed for exploring important raw data (and I did contribute to the design). None of this was done under official reporting structures. But the organization was flexible enough to support the networking, collaboration, and skills needed.
I Did Other Stuff, Too...
Since that graph is so silly, here's a little montage of other exploratory data and design work I did while I was with that client. Lots of tools were involved, from R to Tableau to Flex to Python to Excel to Illustrator. Vive la toolset!