Musing over data science

You have to give it to the computer scientists. They are like the branding agencies of the engineering world. Everything they touch turns to PR gold. Steve Jobs, of course, was their standard bearer, with his infamous "reality distortion field". Now, they are invading the statistician's turf, and have already re-branded us as "data scientists". MIT Technology Review noted this event recently.

Data science

Machine learning

Knowledge discovery

Neural networks

Pattern recognition

Artificial intelligence

Statistics, statistical science, management science, decision science, operations research, even analytics, pale in comparison.

I'm impressed.

***

The blogger at Tibco Spotfire quoted me in this piece about "How to speak like a data scientist".

Perhaps I wasn't as clear in that source as I was in some of my other presentations. The biggest challenge for any analytics (oops, data science) role is how to communicate the statistical information to a non-technical audience. There is a language barrier. One side speaks mathematics, the other side speaks English. Two strategies are available. You could teach the one side English so both sides speak English; or you could teach the other side mathematics so both sides speak mathematics.

All too often, technical people use the second strategy. All too often, the second strategy fails. My biggest advice to you is to try the first strategy.

Comments

[Paulsalvaggio]

Hi Kaiser,

I enjoyed reading your thoughts on data science via your blog, video interviews and book. Given the growing importance of BI across all industries, I agree that even if analysts learn to speak "English", the non-statistician needs to have a strong sense of what analytics can accomplish. It's a two way street.

Paul

btw: I noticed that Spotfire's editor linked the "How to speak like a data scientist" post to your clarification above.

[Kaiser]

Hi Paul, I forgot to mention that I learned about the Spotfire post from your tweet. Thanks for the tip.