Publications

Steve Miller, DM Review Online, November 26, 2008

This is the first in a series of columns on statistical learning for business intelligence (BI). Column one contrasts what�s come to be called statistical or machine learning (ML) with traditional statistical (TS) methods for predictive modeling. Subsequent columns will discuss many of the latest statistical learning procedures and identify software for deployment. Illustrations will be drawn from business applications.

My fondness for the R statistical computing platform is no secret. R combines two current passions at OpenBI: statistical analysis and open source software. Both the R platform and community appear to be growing exponentially in the academic, research and, more recently, business worlds. Increasingly, the benefits of this growth are manifest to BI.

One advantage of working with R is the collateral learning derived from participation in the various support lists. The lists certainly help me keep up to date with coding tricks, best practices, bug resolution, new release schedules and the availability of the latest statistical packages from the worldwide community. But occasionally correspondence evolves to important philosophical discussion of an issue pertinent to statisticians and business analytics professionals alike.

Read more.