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 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