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R Programming Language

The programming language

10 Amazing Data Analytics Platforms Everyone Should Know About

February 18, 2015 by Bernard Marr

Big Data Guru column.

The past few years has seen an explosion in the number of platforms available for big data analytical tasks. The open source Hadoop framework is free to use, but very technical to set up and not specialized towards any particular job or industry. To use it in your business, you need a “platform” to operate it from.[read more]

SAS vs. R: The Deeper Dive

May 7, 2014 by Linda Burtch

SAS vs. R.

Last month, I conducted a quick “flash survey” of my network and asked: Which do you prefer to use, R or SAS? I posted the initial results on my blog a few weeks ago, and as promised during the webinar for our Data Scientist Salary Study, we’ve finished up our deeper dive analysis of the data from over 1,000 respondents.[read more]

The Great Debate: SAS vs. R

April 14, 2014 by Linda Burtch

SAS vs. R.

Despite hearing more about R from clients and candidates than ever before, determining whether R was actually more popular than SAS proved difficult. A quick Google search for “R vs. SAS” returns more than a few pages dedicated to each side, as well as several heated LinkedIn discussions relating to the topic, with no definitive answers.[read more]


NoSQL Vs. RDBMS for Interactive Analytics: Leveraging the Right and Left Brain of Data

February 6, 2014 by Soren Riise


Comparing NoSQL and relational databases is lot like comparing the left and right sides of the brain. Too much focus on structural differences and attributes can overshadow the fact that we’re stuck with both sides of the brain and we need both to make the best use of sensory data.[read more]

The Role of Standards in Predictive Analytics: A Series

December 19, 2013 by James Taylor

analytics / shutterstock

I am working on a paper, for publication in early 2014, on the role of standards such as R, Hadoop and PMML in the mainstreaming of predictive analytics. As I do so I will be publishing a few blog posts. I thought I would start with a quick introduction to the topic now and then finish the series in the new year.[read more]

Choosing Your First Programming Language

November 25, 2013 by Bryan Halfpap

What language will you learn?

Every job requires the right kind of tool, and each programming language can be thought of as a separate tool. Just like some tools are good for hammering nails, driving screws, and leveling screws, some programming languages are innately more suitable than others for tasks like designing a website, interpreting text, or reacting to user input.[read more]

Big Data is Not Just Hadoop

November 11, 2013 by Steve Sarsfield

If you have a big data problem that needs to be solved, don’t jump right on the Hadoop bandwagon. Consider the impact that big data will have on your solutions and on your teams and take a long look at the new generation of columnar data storage and SQL-centric analytical platforms to get the job done.[read more]

Fantasy Football Modeling with R

October 17, 2013 by David Smith

My model pulls aggregated expert rankings from fantasypros, and I pass that data into a machine learning clustering algorithm called a gaussian mixture model to find tiers of players each week. Then I plot them in two dimensional space and the result is charts that let you easily decide your line up each week.[read more]

Big Data Bytes: How Open Source is Changing Business

September 25, 2013 by David Smith

I had a fun time on Friday in a Google Hangout chat with David Pittman (IBM), Eric Kavanagh (Bloor Group) and Tom Deutsch (IBM), where we talked about how open source is changing business. The conversation covered several open source projects including R and Hadoop.[read more]

Putting the R in Cloudera and Hortonworks Hadoop

September 17, 2013 by David Smith

Hortonworks and Revolution Analytics have teamed up to bring the predictive analytics power of R to Hortonworks Data Platform. Hadoop, being a disruptive data processing framework, has made a large impact in the data ecosystems of today. Enabling business users to translate existing skills to Hadoop is necessary to encourage the adoption and allow businesses to get value out of their Hadoop investment quickly.[read more]

Alpha Testing RevoScaleR Running in Hadoop

September 16, 2013 by David Smith

At Revolution Analytics our mission is to establish R as the driver for Enterprise level computational frameworks. In part, this means that a data scientist ought to be able to develop an R based application in one context, e.g. her local PC, and then get it moving by changing horses on the fly (so to speak) and have it run on a platform with more horsepower with minimum acrobatics.[read more]

R User Groups Update

September 9, 2013 by David Smith

User groups are a great strength of the R community. Nothing beats face-to-face encounters with people from other companies and industries who are doing things with R that you haven’t thought about. It is one thing to come across a new application or some useful code on the web, but it is a much richer experience to have someone show you.[read more]

Poll: R Is the Top Language for Data Science 3 Years Running

September 3, 2013 by David Smith

KDDNuggets has completed its annual poll of top languages for analytics, data mining and data science, and just as in the prior two years the R language is ranked the most popular. R is used by almost 61% of respondents. R's usage grew year over year as well, up 16% compared to the 2012 poll.[read more]

High-Performing Predictive Analytics with R and Hadoop

July 17, 2013 by David Smith

Predictive analytics slideshare

Mario Inchosa gave a standing-room-only talk on high-performance predictive analytics in R and Hadoop at last month's Hadoop Summit. In the talk, he described some of the progress we've made integrating the ScaleR parallel external-memory algorithms into the Hadoop platform.[read more]

A Comprehensive Guide to Time Series Plotting in R

June 28, 2013 by David Smith

Time series plotting in R / shutterstock

As R has evolved, its capabilities have improved in every area. The visual display of time series is no exception: as the folks from Timely Portfolio note that "Through both quiet iteration and significant revolutions, the volunteers of R have made analyzing and charting time series pleasant."[read more]