Analytics on Harlan D. Harris

A/B Testing and Product Ratings, Part 1: Delays and Bias

harlan@harris.name (Harlan Harris) — Sun, 22 May 2022 00:00:00 +0000

Suppose you’re a data scientist at an e-commerce web site that sells shoes, responsible for supporting A/B tests. Many A/B tests are easy, and there are a number of companies that sell tools that make the easy cases as simple as clicking a few buttons and looking at pretty graphs. But A/B tests can get statistically complex surprisingly quickly, which is why hiring data scientists with a strong statistics background can make a big difference in the quality of decisions.

Conference Blogging INFORMS Analytics 2017

harlan@harris.name (Harlan Harris) — Sun, 09 Apr 2017 00:00:00 +0000

This post was originally published on Medium

A particularly good way to get a little more out of professional conferences is to blog about your experiences, I think. It makes you focus your thoughts on things like “what’s the big take-away here,” and “what should I be asking people in the hallways?” Rather than just summarizing what you saw, or making snarky Twitter comments (also worth doing!), a great conference blog post is synthesis — combining insights from multiple presentations and conversations into a coherent new whole that helps clarify ideas.

INFORMS Business Analytics 2014 Blog Posts

harlan@harris.name (Harlan Harris) — Sat, 02 Aug 2014 00:00:00 +0000

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.

Communication and the Data Scientist

harlan@harris.name (Harlan Harris) — Tue, 23 Oct 2012 00:00:00 +0000

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

harlan@harris.name (Harlan Harris) — Sat, 16 Jun 2012 00:00:00 +0000

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.

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

harlan@harris.name (Harlan Harris) — Thu, 10 May 2012 00:00:00 +0000

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!

Data Science, Moore's Law, and Moneyball

harlan@harris.name (Harlan Harris) — Tue, 27 Sep 2011 00:00:00 +0000

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”.

hacking .gov shortened links

harlan@harris.name (Harlan Harris) — Sat, 30 Jul 2011 00:00:00 +0000

This past Friday, the web portal to the US Federal government, USA.gov, organized hackathons across the US for programmers and data scientists to work with and analyze the data from their link-shortening service. It turns out that if you shorten a web link with bit.ly, the shortened link looks like 1.usa.gov/V6NpL (that one goes to a NASA page). And because this service was paid for by taxpayer money, the data about each clickthrough is freely available.

making meat shares more efficient with R and Symphony

harlan@harris.name (Harlan Harris) — Mon, 09 May 2011 01:01:01 +0000

In my previous post, I motivated a web application that would allow small-scale sustainable meat producers to sell directly to consumers using a meat share approach, using constrained optimization techniques to maximize utility for everyone involved. In this post, I’ll walk through some R code that I wrote to demonstrate the technique on a small scale.

Although the problem is set up in R, the actual mathematical optimization is done by Symphony, an open-source mixed-integer solver that’s part of the COIN-OR project. (The problem of optimizing assignments, in this case of cuts of meat to people, is an integer planning problem, because the solution involves assigning either 0 or 1 of each cut to each person. More generally, linear programming and related optimization frameworks allow solving for real-numbered variables.) The RSymphony package allows problems set up in R to be solved by the C/C++ Symphony code with little hassle.

making meat shares more efficient

harlan@harris.name (Harlan Harris) — Mon, 09 May 2011 00:00:00 +0000

A personal interest I have is the ethical and sustainable production of food. I’ve been a member of and helped run Community Supported Agriculture groups, and my wife and I currently purchase the majority of our meat from a group of upstate NY pastured-livestock producers who sell their products through CSAs. It’s an ala-carte business model, where I place an order on a website, and the next week I pick up the frozen products cut and packaged as if for retail.

intuitive visualizations of categorization for non-technical audiences

harlan@harris.name (Harlan Harris) — Mon, 25 Apr 2011 00:00:00 +0000

For a project I’m working on at work, I’m building a predictive model that categorizes something (I can’t tell you what) into two bins. There is a default bin that 95% of the things belong to and a bin that the business cares a lot about, containing 5% of the things. Some readers may be familiar with the use of predictive models to identify better sales leads, so that you can target the leads most likely to convert and minimize the amount of effort wasted on people who won’t purchase your product. Although my situation doesn’t have to do with sales leads, I’m going to pretend it does, as it’s a common domain.

My data is many thousands of “leads”, for which I’ve constructed hundreds of predictive features (mostly 1/0, a few numeric) each. I can plug this data into any number of common statistical and machine learning systems which will crunch the numbers and provide a black box that can do a pretty good job of separating more-valuable leads from less valuable leads. That’s great, but now I have to communicate what I’ve done, and how valuable it is, to an audience that struggles with relatively simple statistical concepts like correlation. What can I do?