12 February 2014
This was my second day at Strata.
The keynotes at Strata were very short, 5-10 minutes each. This was a mixed blessing; presenters were brief, but many of them used nothing but data buzzwords. I was strongly reminded of The Worst Speech in the World.
P (quality) = (reality / buzzwords + reality).
Farrah Bostic's argument was "How we decide what to measure, matters." Market research, surveys and focus groups are more biased than we think, leading to flawed decisions and flawed results. I've seen the results of this anecdotally, when people making decisions using the data they had rather than the problem they had.
David Epstein had two points. The first is that before collecting tons of data, you should determine what is important and what can be changed. Collecting data, and then analyzing it, should enable change that is possible. His second point was the famous "10,000 hours of practice" study was based on a flawed study of 40 gifted violinists; it isn't generally applicable. Even the original researcher, K.A. Ericsson, called the hype around the 10,000 hours idea "the danger of delegating education to journalists."
A challenge with data problems, like software engineering, is to reduce the time between idea and product. One huge bottleneck is the cognitive/human time required to build a good model from data.
Building a good model requires iterating over 2 steps:
The second step can be streamlined, even automated.
(IMAGE OF SKYNET)
For everything but the largest data sets, it is computationally/economically possible to run hundreds, even thousands, of machine learning models on a data set and use statistical methods to identify the best ones.
This leaves us with the first step: generating features.
One of the big lessons in machine learning is more data trumps a complicated model. This is exemplified in the seminal paper "The unreasonable effectiveness of data."
Chris' ideas are brought to fruition in DeepDive, a system that has a user define features, but not machine learning or statistics. The tool does all of the machine learning and statistical analysis, and then shows the results. It's already been used on paleobiology data (extracting data from PDF-formatted research papers) with promising results.
I'll be following this closely.
"How do we make sure we're solving the right problem?"
Data scientists aren't the first to ask that question. Designers have this problem all the time, worse than we do. Vague, conflicting requests are a fact of life.
Borrowing from designers and their scoping framework can:
Convincing people of something, even with data, is a form of argument. Data scientists can benefit from 2500 years of work in the humanities, rhetoric, and social sciences.
Knowing the structure of an argument can help with:
This was the most intellectual of the sessions I attended, and one of the most helpful.
In contrast, Monica Rogati's session was lighthearted and utterly entertaining. This was an amazing example of telling a story using data.
The topic? Sleep.
As a data scientist for Jawbone, Monica is effectively running the world's largest sleep study, with access to 50 million nights' sleep. Some findings:
I'll be looking at this session again, looking for presentation tips.
That's it for tonight. Until tomorrow, data nerds!