The annual international R statistical computing four-day conference held this year at UCLA from June 30 to July 3 provided ample opportunities to appreciate what data analysis can provide businesses of all kinds.
Examples that I learned ranged from figuring out when a cargo ship en route should divert its port destination in order to be assured of an open berth to analyzing different weather conditions that could have contributed to airplane departure delays at specific airports.
While June 30 offered all-morning and all-afternoon tutorial sessions, the following days provided keynote speakers, sponsor presentations, and numerous 90-minute tracks in different subject areas, including business. (Having once been a product manager for Max Factor International, I especially appreciated the presentation by a data scientist from L’Oreal.)
David L. McArthur, PhD, MPH, Department of Neurosurgery, David Geffen School of Medicine at UCLA, served as the chair of the useR!2014 Organizing Committee for the conference.
The R statistical computing language is open source, and many of the conference’s presenters discussed open source packages they had created to expand the data analysis abilities of R. Downloading R and the open source packages can be done at http://cran.us.r-project.org/
Here is information from the home page of this link:
R is ‘GNU S’, a freely available language and environment for statistical computing and graphics which provides a wide variety of statistical and graphical techniques: linear and nonlinear modelling, statistical tests, time series analysis, classification, clustering, etc. Please consult the R project homepage for further information.
CRAN is a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R. Please use the CRAN mirror nearest to you to minimize network load.
Takeaway from the conference for companies of all sizes:
Whether your company uses R alone, in combination with other statistical programs, or uses only other statistical programs, in the future those companies who do extensive data analysis will probably have a strong advantage over competitors who do not do extensive data analysis.
Of course, there is one caveat to this prediction:
Data analysis must be accompanied by thoughtful consideration of the data inputs and data outputs in the context of real people acting in a real world.
(c) 2014 Miller Mosaic LLC
Phyllis Zimbler Miller has an MBA from Wharton and is a digital marketer. Learn more about her on LinkedIn at www.linkedin.com/in/phylliszimblermiller