The document discusses the use of R programming for business intelligence and data analysis. It describes how R provides more advanced analytical tools than Excel or SAS to help businesses understand customer data. Revolution Analytics aims to promote commercial use of R and provides tools like ScaleR to help businesses process large datasets using parallel processing across multiple servers. The document also highlights how R allows data scientists at companies like Facebook to quickly clean and explore new datasets, and how talent familiar with R from academia can help businesses leverage data.

2. RPROGRAMMING
1. BUSSINESS INTELLIGENCE
2. THE R EVANGELISTS
3. DEALING WITH TONS OF BUSINESS DATA
4. COOL GRAPHICS
5. TALENT IS EVERYWHERE

3. BUSINESS INTELLIGENCE
• Every business is trying to figure out the best way to understand
information about their customers and themselves. But simply using
Excel pivot tables to analyze such quantities of information is absurd, so
many companies use the commercially available tool SAS to cull
business intelligence.
• But SAS is no match for the open-source language that pioneering data
scientists use in academia, which is simply known as R.
• The R programming language leans more frequently to the cutting edge
of data science, giving businesses the latest data analytis tools.

4. THE R EVANGELISTS
• At least one company thinks R is ready for commercial prime.RedHat
is to Linux and Cloudera is to Hadoop, Revolution Analytics is to the R
language in the commercial world
• Several years ago, David Smith, chief community officer at Revolution
Analytics, noticed that a lot of academics and students used R but saw
less usage in industry. “At the time, there was no company there to
support R, provide expertise around R, or provide any kind of
commercial backing for R. So that’s how Revolution Analytics was
founded,” says Smith

5. DEALING WITH TONS OF BUSINESS DATA
• Here’s what their packages actually do. One, ScaleR, helps
businesses go through all of their data by scaling it to work on parallel
processors.
• Using standard R packages, machines will run out of memory when
dealing with such large amounts of data, but ScaleR repurposes the
data to process chunks of it on different servers simultaneously. Smith
calls this parallel processing algorithm its “secret sauce.”

6. COOL GRAPHICS
• Generally, we use R to move fast when we get a new data set, says
Solomon Messing, data scientist at Facebook. With R, we don’t need
to develop custom tools or write a bunch of code. Instead, we can
just go about cleaning and exploring the data.
• The data can range from something like News Feed numbers to
correlations with the amount of Facebook friends a user has.
Although these packages are not commercial, Revolution Analytics
has kept tabs on Facebook’s R usage for some time

7. TALENT IS EVERYWHERE
• In school, data scientist Casey Herron studied statistics and came to
Revolution Analytics with an already intimate understanding of R. Having
used R as an undergraduate, she continued using it in her master’s
program and when she moved into her first job after graduate school, as
a statistician. She has now been at Revolution Analytics for 10 months
• I think the number one value to businesses [in using R] is access to
talent,” says Smith. “So many businesses now are doing much more with
data, especially with the big data revolution and doing much more with
analytics. And because they’re hiring people coming out of school. They
know R already

8. Thank You
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