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