Tag Archives: data science

Patterns for Connecting Predictive Models to Software Products

This was originally published on Medium on June 21st, 2016.


You’re a data scientist, and you’ve got a predictive model — great work! Now what? In many cases, you need to hook it up to some sort of large, complex software product so that users can get access to the predictions. Think of LinkedIn’s People You May Know, which mines your professional graph for unconnected connections, or Hopper’s flight price predictions. Those started out as prototypes on someone’s laptop, and are now running at scale, with many millions of users.

Metaphor (source)

Even if you’re building an internal tool to make a business run better, if you didn’t build the whole app, you’ve got to get the scoring/prediction (as distinct from the fitting/estimation) part of the model connected to a system someone else wrote. In this blog post, I’m going to summarize two methods for doing this that I think are particularly good practices — database mediation and web services.

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Simulating Rent Stabilization Policy at the National Day of Civic Hacking

This post was originally published on Medium on June 5, 2016.


Yesterday was the 2016 National Day of Civic Hacking, a Code for America event that encourages people with technology and related skills to explore projects related to civil society and government. My friend Josh Tauberer wrote a thoughtful post earlier about the event called Why We Hack —on what the value of this sort of event might be — please read it.

For my part, this year I worked on one of the projects he discusses, understanding the impact of DC’s rent stabilization laws and what potential policy changes might yield. As Josh noted, we discovered that it’s a hard problem. Much of the most relevant data (such as the list of properties under rent stabilization and their current and historical rents) are not available, and have to be estimated. Getting to a realistic understanding of the impact of law and policy on rents seems incredibly valuable, but hard.

So I spun off the main group, and worked on an easier but much less ambitious project that could potentially be useful in just an afternoon’s work. Instead of trying to understand the law’s effect on actual DC rents, I built a little tool to understand the law’s effect on a rather unrealistic set of simulated apartment buildings. Importantly, I did this fully aware that I’m not building with, I’m tinkering; my goal was to do something fun and interesting that might lead to something substantial and usable later, probably by someone else.

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Thoughts on Managing Data Science Team Workstreams (and a Shiny app)

This is an updated version of a post originally published on Medium on Jan. 28, 2016. I may have more to say about this sort of thing in the near future.


There are different types of data scientists, with different backgrounds and career paths. With Sean Murphy and Marck Vaisman, I wrote an article about this for O’Reilly a few years back, based on survey research we’d done. Download a copy, if you haven’t read it. This idea is now pretty well established, but I want to talk about a related issue, which is that the type of work that Data Science teams do varies a lot, and that managing those types of work can be an interesting challenge.

As Josh Wills said, data scientists aren’t software developers, but they sometimes do that sort of work, and they aren’t statisticians, but they sometimes do that sort of work too. At EAB, where I lead a Data Science team of people with very diverse backgrounds and skill sets, this issue leads to a lot of complexity and experimentation as we (and the upper management I report to) try to ensure that everyone is working on the right tasks, at the right time, efficiently.

In this post, I’d like to share some thoughts about how we currently think about and manage different types of Data Science work. I also wrote a little Shiny web tool to help us manage our time, and I’ll show that off as well.

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Building a Complementary Data Science Team

This is an updated version of an article first posted on Medium on Nov. 23, 2015. I’ve disabled the links to the jobs, as those specific ones are no longer available. If you’re interested in a role at EAB or the Advisory Board, please get in touch, though!


I’m the Director of Data Science at EAB, a firm that provides best-practices research and enterprise software for colleges and universities. My team is responsible for the predictive models and other advanced analytics that are part of the Student Success Collaborative product that’s used by academic advisors and other campus leadership. We’re hiring data scientists, and I wanted to publicly say a few things about the roles we have advertised. (Note that EAB is part of a public company and is in a competitive market, so there are obviously things I’m not saying!)

The most important point is that data scientists specialize, so look for the specializations. My co-authors and I made this point in our 2012 e-book Analyzing the Analyzers, and the folks at Mango Solutions are burning up Twitter with their self-service tool for identifying data science strengths and weaknesses.

Drew Conway’s Data Science Venn Diagram

A related point is that existing framing devices can help you balance a team. Drew Conway’s Venn Diagram remains a great way to think about Data Science aptitude. Combine people with strengths in each part of the diagram, who know enough to collaborate effective and make each other stronger, and you don’t need a team of unicorns with 3 PhDs each.

I suspect the details of the framing device are less important than the fact that you have one. It forces you to think about variety and complementary skills, and how people work together to solve problems and build systems.


At EAB, we have four career tracks for data scientists — Research, Engineering, Statistical Programming, and Management. Our new roles supplement our existing team by adding several new people, each with different capabilities and seniority.

At a Senior level, we’re looking for a Statistical Programmer-track person who is particularly strong in algorithm development and implementation, perhaps a straight-up Computer Scientist. Think of the “Machine Learning” area in Drew’s diagram. As we look to expand the classes of statistical techniques that we use, we need more people who know the academic literature and can figure out exactly what technical solution will let us build and scale high-quality models. Interested? Please apply!

A little less senior, we’re also looking for a Researcher who can help us apply domain knowledge even more effectively in our analyses, models, and systems. Some software, data visualization, and statistical skills required — maybe a quantitative Social Scientist pivoted into industry? The upper edge of the Substantive Expertise area. Sound like you? Please apply!

I strongly believe that a Data Science team should do all of the Data Science, including building and owning models in production. So, last but not least, we’re looking for another Engineering-focused data scientist, who can help us build model frameworks, data tools, workflow tools, and more. This role can be junior or even entry-level, but we do need programming skills, statistical thinking skills, and some sort of portfolio. Programmer and recent data science boot camp grad, perhaps? Please apply!

Of course, as we talk to people, learn what they’re good at and excited about, and what they bring, we may end up with a different mix of skills. But regardless, they’ll cover the space of data scientists, will provide different perspectives and skills, and will help us own our own tools and systems so that we can move and learn quickly.

INFORMS Business Analytics 2014 Blog Posts

Earlier this year, I attended the INFORMS Conference on Business Analytics & Operations Research, in Boston. I was asked beforehand if I wanted to be a conference blogger, and for some reason I said I would. This meant I was able to publish posts on the conference’s WordPress web site, and was also obliged to do so!

Here are the five posts that I wrote, along with an excerpt from each. Please click through to read the full pieces:

Operations Research, from the point of view of Data Science

  • more insight, less action — deliverables tend towards predictions and storytelling, versus formal optimization
  • more openness, less big iron — open source software leads to a low-cost, highly flexible approach
  • more scruffy, less neat — data science technologies often come from black-box statistical models, vs. domain-based theory
  • more velocity, smaller projects — a hundred $10K projects beats one $1M project
  • more science, less engineering — both practitioners and methods have different backgrounds
  • more hipsters, less suits — stronger connections to the tech industry than to the boardroom
  • more rockstars, less teams — one person can now (roughly) do everything, in simple cases, for better or worse

What is a “Data Product”?

DJ Patil says “a data product is a product that facilitates an end goal through the use of data.” So, it’s not just an analysis, or a recommendation to executives, or an insight that leads to an improvement to a business process. It’s a visible component of a system. LinkedIn’s People You May Know is viewed by many millions of customers, and it’s based on the complex interactions of the customers themselves.

Healthcare (and not Education) at INFORMS Analytics

[A]s a DC resident, we often hear of “Healthcare and Education” as a linked pair of industries. Both are systems focused on social good, with intertwined government, nonprofit, and for-profit entities, highly distributed management, and (reportedly) huge opportunities for improvement. Aside from MIT Leaders for Global Operations winning the Smith Prize (and a number of shoutouts to academic partners and mentors), there was not a peep from the education sector at tonight’s awards ceremony. Is education, and particularly K-12 and postsecondary education, not amenable to OR techniques or solutions?

What’s Changed at the Practice/Analytics Conference?

In 2011, almost every talk seemed to me to be from a Fortune 500 company, or a large nonprofit, or a consulting firm advising a Fortune 500 company or a large nonprofit. Entrepeneurship around analytics was barely to be seen. This year, there are at least a few talks about Hadoop and iPhone apps and more. Has the cost of deploying advanced analytics substantially dropped?

Why OR/Analytics People Need to Know About Database Technology

It’s worthwhile learning a bit about databases, even if you have no decision-making authority in your organization, and don’t feel like becoming a database administrator (good call). But by getting involved early in the data-collection process, when IT folks are sitting around a table arguing about platform questions, you can get a word in occasionally about the things that matter for analytics — collecting all the data, storing it in a way friendly to later analytics, and so forth.

All in all, I enjoyed blogging the conference, and recommend the practice to others! It’s a great way to organize your thoughts and to summarize and synthesize your experiences.

Why a Data Community is Like a Music Scene — Resources

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:

 

More posts on the Data Community DC blog

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.

 

Communication and the Data Scientist

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

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

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!