This Is How Rprogramming Will Look Like In 10 Years Time

1
This Is How Rprogramming Will Look Like In 10
Years Time
2
Rprogramming

• BUSSINESS INTELLIGENCE
• THE R EVANGELISTS
• DEALING WITH TONS OF BUSINESS DATA
• COOL GRAPHICS
• 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. The problem With
  loose standards and scores of diverse
  contributors, it is shaky ground for business

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 thats how Revolution Analytics
  was founded, says Smith

5
DEALING WITH TONS OF BUSINESS DATA

• Heres 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 dont 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 Facebooks 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 masters 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
  theyre hiring people coming out of school. They
  know R already