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A great read. Thesis of the article: "Insightful statisticians have for at least 50 years been laying the groundwork for constructing that would-be entity as a

A great read. Thesis of the article: "Insightful statisticians have for at least 50 years been laying the groundwork for constructing that would-be entity as an enlargement of traditional academic statistics. This would-be notion of Data Science is not the same as the Data Science being touted today, although there is significant overlap. The would-be notion responds to a different set of urgent trends - intellectual rather than commercial. Facing the intellectual trends needs many of the same skills as facing the commercial ones and seems just as likely to match future student training demand and future research funding trends" "Data Science" is currently a field where corporations are winning in defining what activity academic universities should support. This article is a nice pull back in the other direction, and focusing on the intellectual rather than commercial elements.

mikk14 11y ago
Not sure if I completely agree. The problem, as I see it, is that corporations are one of the most important data collectors out there, and sometimes most accessible. Personally, I am doing research with data from supermarket chains, credit card companies and telecommunication operators. However, I always strive to make the paper mainly about an intellectual challenge (central place theory, modeling inclusive economic growth, studying effects of the dynamics human mobility, ...) and just use the commercial application as a side note. So, even if the commercial intent is there and the corporation interest is visible in the paper, the attempt is to use it just as a "price to pay" for increasing a "more general" knowledge. Unfortunately, if I look at the (sometimes very commendable!) efforts of public data release from, e.g., governments (data.gov, data.gov.uk, etc) there always some shortfalls. They are either too flat -- a classic census table, which is of limited interest because you can deal with it without the need of innovative data analysis techniques -- or just not large or deep enough to get a big picture from them. For instance, in human mobility, a government will provide you a mobility survey, a sample of self-reported trips recalled from memory for only one target day. But a phone call record can give you all people, for a long period of time, without relying on people's faulty memory of their movements. If you want to understand, e.g., how disease spreads in a city, or how people are cut out by public transportation and infrastructure shortcomings, the survey results are necessarily going to be worse than the ones you'd get with call metadata.

mikk14 11y ago

Not sure if I completely agree. The problem, as I see it, is that corporations are one of the most important data collectors out there, and sometimes most accessible. Personally, I am doing research with data from supermarket chains, credit card companies and telecommunication operators. However, I always strive to make the paper mainly about an intellectual challenge (central place theory, modeling inclusive economic growth, studying effects of the dynamics human mobility, ...) and just use the commercial application as a side note. So, even if the commercial intent is there and the corporation interest is visible in the paper, the attempt is to use it just as a "price to pay" for increasing a "more general" knowledge. Unfortunately, if I look at the (sometimes very commendable!) efforts of public data release from, e.g., governments (data.gov, data.gov.uk, etc) there always some shortfalls. They are either too flat -- a classic census table, which is of limited interest because you can deal with it without the need of innovative data analysis techniques -- or just not large or deep enough to get a big picture from them. For instance, in human mobility, a government will provide you a mobility survey, a sample of self-reported trips recalled from memory for only one target day. But a phone call record can give you all people, for a long period of time, without relying on people's faulty memory of their movements. If you want to understand, e.g., how disease spreads in a city, or how people are cut out by public transportation and infrastructure shortcomings, the survey results are necessarily going to be worse than the ones you'd get with call metadata.