Why a Data Community is Like a Music Scene — Resources

October 26th, 2013 No comments

On Monday, October 28th, 2013, I gave a 5-minute Ignite talk entitled “Why a Data Community is Like a Music Scene” at an event associated with the Strata conference. Here’s the video:

And here are the acknowledgements and references for the talk:

Data Community DC

How Music Works, by David Byrne

my slides for the Ignite talk

my blog post (written first)

Photos:

 

Bikeshare hills, incentives, and rewards

June 23rd, 2013 1 comment

A topographic map of Washington in 1791 by Don Alexander Hawkins. I live on the top edge of the map, on one of those hills.

I’m a generally happy user of DC’s Capital Bikeshare system — just renewed my annual membership today in fact. But I don’t use it as much as I’d like to, for one critical reason. I live on top of a hill. Riders are happy to take bikes from the neighborhood to their jobs downhill, but are much less likely to ride them uphill. As a result, the bike racks in my neighborhood are frequently completely empty by 8:00 or 8:30am, despite the many stores and businesses in the area. The only days I can reliably take a bike into work are when I leave at 7:15 for 8:00 meetings, which is thankfully not too often. On several occasions I have looked at the handy real-time map of bikeshare bikes, only to observe that there are no bikes available within a 15 minutes walk of my home!

What should Bikeshare do to solve this problem? Well, they already do one thing, which is that they hire people to put bicycles in the back of a big van, then drive them up the hill to rebalance the system. This works, but it’s expensive for the system, and it’s not very timely or efficient. In other transportation problems, incentives are used to balance demand. For instance, airlines and Amtrak use pricing to incentivize people who are flexible in their schedule to take off-peak trips. But that won’t work for Bikeshare, as most rides are free. (I pay $75/year, but all trips of 30 minutes or less are free. My rides are mostly 15-25 minutes long.) So people happily ride downhill to their downtown jobs in the mornings, but don’t ride uphill to their reverse-commute jobs, and don’t as often ride uphill in the evening home either. The end result is unhappy customers and excessive costs for the Bikeshare system.

ch_map

Rough map of a possible incentive line for North-Central DC.

So if you can’t give people the usual financial incentives to drop off bikes in the Columbia Heights rack at 8am, what can you do to reduce the need for rebalancing and provide reasons for people to want to help solve Bikeshare’s problem? I think the answer is swag. Imagine that there were lines on the Bikeshare map. Every time you crossed the line going in an uphill direction (reducing the need for rebalancing), you’d earn some points. If you earned enough points, you could redeem them for Bikeshare-branded, limited-edition swag. Imagine a t-shirt in official CaBi colors that said “I bike up hills”, available only through this point system. Who wouldn’t want that?

It’s easy for Bikeshare to figure this out, as they know exactly where you picked up each bike, and where you dropped it off. Determining whether you crossed a line, and thus biked uphill, is easy. And in addition to making people excited about biking up hills, you get them wearing branded items of clothing, which can only help market the system more broadly. They already sell swag through a Cafepress shop, so much of the infrastructure is in place. It’s a win-win.

Bikeshare people, if you read this and think it’s a good idea, please run with it!

Screenshot - 6_23_2013 , 3_08_23 PM

What the swag might look like.

 

 

On .name and third-level domains

May 15th, 2013 5 comments

And, we’re back! After being off-line for several weeks, this site is now live again! I can’t imagine you missed it.

Here’s what happened. Let’s start at the beginning. In 2003, ICANN added .name to the list of top-level domains (like .com, .edu, etc.). The idea is that individuals would use it for personal sites and email addresses. You can still do this, but (in case you haven’t noticed), it’s not very popular, and most domain name registrars don’t even sell .name addresses.

I purchased harlan.harris.name in 2003. Unlike .com addresses, you don’t generally buy second-level domains in .name, you buy third-level domains. (.name is the top-level domain, harris.name is the second-level domain, which you can’t buy, and harlan.harris.name is the third-level domain.) A cool feature is that if you buy a.b.name, you can get the email address a@b.name, not something like me@a.b.name (although you can set that up too). So my email address has been harlan@harris.name for ten years.

Fast forward to April, 2013. I notice that my personal web site (where you are now) has been replaced by a generic sales screen. You know, with a bunch of random keywords, a stock photo, and “buy this domain!” in big red print. Not good. At first I thought that my WordPress site (which hosts this blog) had gotten hacked, but no such luck. It turns out to be a convoluted mess of broken technology and confused customer support reps. The fortunate thing is that I don’t use this site extensively, and the problem with the web forwarding didn’t seem to affect my email address forwarding, so I didn’t lose any email.

The simplified version of what happened is that the company I bought the domain from in 2003, PersonalNames, merged with a company called Dotster a year or two ago. They presumably merged their technical systems together, which makes sense. But they for some reason failed to properly set up a system for third-level .name domain administration. And so my account failed to get properly transferred into their systems, and they stopped sending me notices about problems.

Although I still technically owned harlan.harris.name, I could no longer log in and administer it, and the redirection to this web site (at another company, HostGator) was reset at some point for still-unknown reasons.

It took a week and a dozen email messages and several hours on the phone for Dotster to figure out that yes, they owned this domain, but no, they didn’t have the technical chops to administer it.

I then set up an account with another company, eNom (nom, nom…), that does support third-level .name domains. Transferring the domain took another week and three attempts, due to errors on both sides. Add 48 hours for DNS forwarding to propagate around the Internet, and I’m finally back online yesterday!

Except that although my email forwarding still works, I don’t yet have control over that, because Dotster seemingly neglected to transfer email forwarding rights at the same time as the rest of the domain. So if you need me tomorrow, I’ll be back on the phone with tech support.

Sad Rain

Categories: Personal Tags: ,

More posts on the Data Community DC blog

February 21st, 2013 No comments

For those people (or, more likely, 0 or 1 persons) who follow this blog to catch up on my professional thoughts: I’ve been doing a little bit of writing on the Data Community DC blog. Here are all my posts over there: http://datacommunitydc.org/blog/author/harlan/ I’d definitely encourage you to read everyone else’s work on the DC2 blog too!

Two titles of my own:

And three  of others’:

There are also weekly round-up posts on data topics generally, and on data visualization specifically, as well as event previews and reviews, etc.

 

Categories: Professional Tags: ,

Pretzel Whoopie Pies with Vanilla Stout Filling

October 22nd, 2012 No comments

My newish cooking club had a dinner yesterday with the theme American Beer. I was tasked with dessert, and came up with this recipe for Pretzel Whoopie Pies. They turned out extremely well, so I thought I’d share the recipe here.

Sources:

Ingredients:

  • 2 egg yolks
  • 1/2 c minus 1 T sugar
  • 1 T light corn syrup
  • 1/2 c finely ground unsalted mini pretzels
  • 1/2 c cake flour
  • 1 t baking powder
  • 1/8 t salt
  • 4 T butter, softened
  • 1/3 c milk
  • kosher salt
  • 1 c stout beer (Breckenridge Vanilla Porter is excellent)
  • 4 T butter, softened
  • 8 oz powdered sugar

Recipe:

  1. Beat egg yolks, sugar, and corn syrup until lightened.
  2. Mix pretzel flour, cake flour, baking powder, and salt. Beat in butter and milk. Add to liquid mixture and mix thoroughly.
  3. Refrigerate dough for 30 minutes to hydrate evenly. Preheat oven to 350 F.
  4. Drop 12 evenly-shaped cookies onto a silpat-covered pan. A ring mold is helpful.  Sprinkle kosher salt over the top lightly.
  5. Bake about 14 minutes, until starting to brown around the edges, but still soft.
  6. Cool thoroughly on wire racks.
  7. Boil beer in a saucepan large enough to deal with foaming up, and reduce to 1/2 c. Cool to room temperature.
  8. Mix butter, powdered sugar, and reduced beer into a creamy, delicious frosting.
  9. Make sandwiches out of cookies and frosting.

Makes 6 whoopie pies.

Categories: Personal Tags: ,

Communication and the Data Scientist

September 23rd, 2012 No comments

I recently gave a presentation on communication issues around the terms “Data Science” and “Data Scientist”, based in part on a survey that I did with my Meetup colleagues Marck and Sean. The basic idea is that these new, extremely-broad buzzwords have resulted in confusion, which has impacted the ability of people with skills and people with data to meet and effectively communicate about who does what and what appropriate expectations should be. The survey was an attempt to bring some clarity to the issue of who are the people in this newly-reformulated community, and how do they view themselves and their skills. For more on the survey, see our post on the Data Community DC blog. Here’s the video of my presentation at DataGotham:

integrating R with other systems

June 16th, 2012 No comments

I just returned from the useR! 2012 conference for developers and users of R. One of the common themes to many of the presentations was integration of R-based statistical systems with other systems, be they other programming languages, web systems, or enterprise data systems. Some highlights for me were an update to Rserve that includes 1-stop web services, and a presentation on ESB integration. Although I didn’t see it discussed, the new httr package for easier access to web services is also another outstanding development in integrating R into large-scale systems.

Coincidentally, I just a week or so ago had given a short presentation to the local R Meetup entitled “Annotating Enterprise Data from an R Server.” The topic for the evening was “R in the Enterprise,” and others talked about generating large, automated reports with knitr, and using RPy2 to integrate R into a Python-based web system. I talked about my experiences building and deploying a predictive system, using the corporate database as the common link. Here are the slides:

 

Survey of Data Science / Analytics / Big Data / Applied Stats / Machine Learning etc. Practitioners

May 10th, 2012 No comments

As I’ve discussed here before, there is a debate raging (ok, maybe not raging) about terms such as “data science”, “analytics”, “data mining”, and “big data”. What do they mean, how do they overlap, and perhaps most importantly, who are the people who work in these fields?

Along with two other DC-area Data Scientists, Marck Vaisman and Sean Murphy, I’ve put together a survey to explore some of these issues. Help us quantitatively understand the space of data-related skills and careers by participating!

Survey link: http://bit.ly/IQNM5A

It should take 10 minutes or less, data will be kept confidential, and we look forward to sharing our results and insights in a variety of venues, including this blog! Thanks!

Apple TV and cross-device user-interface integration

November 6th, 2011 No comments

On last week’s Build and Analyze — a great podcast nominally about iOS development, but actually more about just living a tech-geek lifestyle — Marco talked a lot about the rumored “Apple TV” and whether it could actually be a groundbreaking product. He concluded that it probably couldn’t. Most people wouldn’t dump a working TV just for an Apple brand; the touch-screen interface that Apple has been using for the iPad and iPhone wouldn’t work for a TV; the only apps that would work well on a TV would be just ways of getting better content (I note that Roku apps are laughable, with the exception of Angry Birds); getting access to better content than other competitors is probably impossible, even for Apple.

For these reasons and more, Marco suggested that there’s little that Apple, or anyone else, could do to substantially improve the TV experience, with the exception of better menu design.

I think there’s a way that Apple (or someone) could integrate modern technology into a TV that would be actually compelling, though. And in some ways it’s the same way that I earlier blogged about for MP3 players. Cross-device user-interfaces. Here’s how it might work for a television:

Read more…

Data Science, Moore’s Law, and Moneyball

September 27th, 2011 6 comments

I’m fond of navel gazing, meta discussions, and so forth. I’ve recently written about inferring navel gazing from link data, and about the meaning of the “Analytics” buzzword. This post will be my second on that other infectious buzzword, “Data Science”.

When I moved to Washington DC in July, I was struck by the fact that there was no Meetup for analytics/applied statistics/machine learning/data science. There’s a great DC Tech Meetup, a great Big Data Meetup, and a great R Meetup, but nothing like the NYC Predictive Analytics Meetup. So, I and a couple of others I talked to about this (Marck Vaisman, who I first met through the NYC R Meetup a couple years ago, and Matt Bryan, who I met just after moving to town), started a new Meetup, which we decided to call “Data Science DC“.

For our second meetup, we thought we should address some aspect of our name, and so I presented a little bit about the term and the controversies around its definition and its recent dramatic upsurge in popularity. Here are the slides (note that you should be able to click through the links on the slide to the source documents):

I mostly didn’t present a personal opinion about what I though the term means, or what it should mean, but instead wanted to present a bunch of other peoples’ points of view to kick off an interesting discussion. And in that sense I succeeded. We had an exceedingly interesting conversation following my slides, and I think a couple of the most interesting ideas from the evening came out of that discussion.

Here are three theses I’d like to propose.

  1. “Data Science” is defined as what “Data Scientists” do. What Data Scientists do has been very well covered, and it runs the gamut from data collection and munging, through application of statistics and machine learning and related techniques, to interpretation, communication, and visualization of the results. Who Data Scientists are may be the more fundamental question.
  2. One reason Data Science is a big thing now is because advances in technology have made it easy for Data Scientists to develop wide-ranging expertise. Even 10 years ago, the idea that the same person could integrate several databases, run a multilevel regression, and generate elegant visualizations would be seen as incredibly rare.
  3. The other reason Data Science is a big thing now is because sabermetrics demonstrated that number-crunching brings results. There’s nothing business leaders love more than a sports analogy, and the analytic revolution in professional sports immediately draw attention to the ways that numbers beat intuition.

I tend to like the idea that Data Science is defined by its practitioners, that it’s a career path rather than a category of activities. In my conversations with people, it seems that people who consider themselves Data Scientists typically have eclectic career paths, that might in some ways seem not to make much sense. A typical path might be someone who started out learning to program, then spent some time in a scientific field, then hopped around a variety of different roles, collecting a wide variety of different skills, all of which related to using analytical techniques to make sense of data.

This sort of career path isn’t particularly new, but what is new is that it’s now possible to relatively quickly and cheaply do get started in all of the processes involved in Data Science. (Thanks to Taylor Horton for suggesting this at the Meetup!) Fast computers, open source tools, and some programming skills allow someone to try a new data management approach or a new machine learning technology incredibly quickly, and to iterate on approaches until a solution to a particular problem is found. This has two consequences. First of all, the productivity of a modern Data Scientist is remarkable. Projects that a few decades ago would have taken teams of people literally years can now be done in a few days. Second of all, this amazing productivity allows people to spend their 10,000 hours developing expertise in the now vertically integrated process of Data Science, rather than having to spend all of that time focusing on developing skills on just a single aspect of the task. There are huge number of things that need to be learned to be an effective Data Scientist, but it is now possible to learn those skills quickly enough to make a career out of being a Jack of all Trades and a near-master at many of them.

So now there’s a supply of people who could be Data Scientists. But what about the type of demand that drives an incessant stream of O’Reilly articles and job postings? Where does the demand come from? Justin Grimes had an intriguing idea that resonated with me — analytics in sports, which I propose as the other reason why analytics and data science have become buzzwords. Although business has used mathematical methods for 100 years, (thousands if you include finance and insurance) the idea that you could hire a very small number of people to analyze data and beat gut instincts in many aspects of decision making is much newer. The idea that a statistician could turn around the Oakland A’s by radically overturning longstanding recruiting practice was a powerful analogy. Even now, business books about analytics almost always have sports examples in the first chapters. I made a point at work the other day by noting that most professional sports prognosticators predict NFL playoff outcomes wrong because they over-weight last years’ results. Sports analogies get attention.

Does this make sense? Data Science is a buzzword now because a group of people with eclectic talents match a growing demand for and recognition of the value of those talents. I’d love feedback on these thoughts!