
A very short history of data science (2012) - sti398
http://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/
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KasianFranks
Favorite quotes:

“…the benefit to the data analyst has been limited, because the knowledge
among computer scientists about how to think of and approach the analysis of
data is limited, just as the knowledge of computing environments by
statisticians is limited. A merger of knowledge bases would produce a powerful
force for innovation. This suggests that statisticians should look to
computing for knowledge today just as data science looked to mathematics in
the past. … departments of data science should contain faculty members who
devote their careers to advances in computing with data and who form
partnership with computer scientists.”

2001 Leo Breiman publishes “Statistical Modeling: The Two Cultures” (PDF):
“There are two cultures in the use of statistical modeling to reach
conclusions from data. One assumes that the data are generated by a given
stochastic data model. The other uses algorithmic models and treats the data
mechanism as unknown. The statistical community has been committed to the
almost exclusive use of data models. This commitment has led to irrelevant
theory, questionable conclusions, and has kept statisticians from working on a
large range of interesting current problems. Algorithmic modeling, both in
theory and practice, has developed rapidly in fields outside statistics.

