What to expect from Strata Conference 2015? An empirical outlook.

In one week, the 2015 edition of Strata Conference (or rather: Strata + Hadoop World) will open its doors to data scientists and big data practitioners from all over the world. What will be the most important big data technology trends for this year? As last year, I ran an analysis on the Strata abstract for 2015 and compared them to the previous years.

One thing immediately strikes: 2015 will be probably known as the “Spark Strata”:


If you compare mentions of the major programming languages in data science, there’s another interesting find: R seems to have a comeback and Python may be losing some of its momentum:


R is also among the rising topics if you look at the word frequencies for 2015 and 2014:


Now, let’s take a look at bigrams that have been gaining a lot of traction since the last Strata conference. From the following table, we could expect a lot more case studies than in the previous years:


This analysis has been done with IPython and Pandas. See the approach in this notebook.

Looking forward to meeting you all at Strata Conference next week! I’ll be around all three days and always in for a chat on data science.

Data Humanities

Mathematics is usually not regared as a science but as part of philosophy - although it has some relation to the "real world" - as shown in this 18 century cut.
Mathematics is usually not regared as a science but as part of philosophy – although it has some relation to the “real world” – as shown in this 18 century cut.
There is a reason why we differentiate science and the humanities. And although sociology, experimental psychology and even history nowadays deploy many scientific methods, the difference is still fundamental. Humantites deal with correlations; the causalities are way further speculative than the “laws of nature” that are formulated in physics or chemistry. Also the data that supports social research is always and inherrently biased, no matter how much care we take in sampling, representativeness and other precautions we might take.

In her remarkable talk at Strataconf, Kate Crawford warned us, that we should always suspect our “Big Data” sources as highly biased, since the standard tools of dealing with samples (as mentioned above) are usualy neglected when the data is collected.

Nevertheless, also the most biased data gives us valuable information – we just have to be careful with generalizing. Of course this is only relevant for data relating to humans using some kind of technology or service (like websites collecting cookie-data or people using some app on their phone). However, I am anyway much more interested in the humanities’ side of data: Data describing human behavior, data as an aditional dimension of people’s lives.

Taken all this, I suggest to call this field of behavior data “Data Humanities” rather than “Data Science”.

Prediction vs. Description or: Data Science vs. Market Research

“My market research indicates that 50% of your customers are above the median age. But the shocking discovery was that 50% were below the median age.”
(Dilbert; read it somewhere, cant remember the source)

It was funny to see everyone at O’Reilly’s Strata Conference talk about data science and hear just the dinosaurs like Microsoft, Intel or SAP still calling it “Big Data”. Now, for me, too, data science is the real change; and I tell you, why:

What always annoyed me when working with market researchers: you never get an answer. All you get is a description of the sample. Drawing samples was for sure a difficult task 50 years ago. You had to send interviews arround, using a kish grid (does anyone remember this – at least outside Germany?). The data had to be coded into punch cards and clumsy software was used to plot elementary descriptives from ascii-letters. If you still use SPSS, you might know what I am talking about. When I studied statistics in the early 90s, testing hypotheses was much more important than predictions, and visualisaton was not invented yet. The typical presentation of a market researcher would thus start with describing the sample (50% male, 25% from 20 to 39 years, etc.) and in the end, they would leave the client with some more or less trivialy aggregated Excel-Tables.

When I became in charge of pricing ad breaks of a large TV network, all this research was useless for my purposes. My job required predicting the measured audiences of each of the approximately 40 ad breaks for every of our four national stations six weeks in advance. I had to make the decission in real time, no matter how accurate the information I calculated the risks on would have been.

Market research is bad in supporting real time management decissions. So managers tend to decide on their “gut feelings”. But the framework has changed. The last decade brought to us the possibility to access huge data sets with low latency and run highly multivariate models. You cant do online advertising targeting based on gut feelings.

But most market researchers would still argue that the analytics behind ad targeting are not market research because they would just rely on probabilistic decissions, on predictions based on correlations rather than causality. Machine learning does not test a hypothesis that was derived from a theoretical construct of ideas. It identifies patterns and the prediction would be taken as accurate just if the effect on the ROI would be better then before.

I can very well live with the researchers keeping to their custom as long as I may use my data to do the predictions I need. When attending Strata Conference, I realized this deep paradigm shift from market research, describing data as its own end to data science, getting to predicitons.

Maybe it is thus a good thing to differentiate between market research and data science.

(This is the first in a row of posts on our impressions at Strata this year; the others will follow quickly …)