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We had a great conversation about the Big Data revolution, and how large enterprises are catching up with the likes of Google and Facebook when it comes to using their data stores to improve their operations and provide a personalized experience for their customers. The key to making this possible is data science using the R language, and the speed, scalability and enterprise readiness provided by Revolution R Enterprise.
You already know that R is an amazingly powerful language for data analysis, but what if you're not a programmer? Or, what if you want to make the data manipulations, visualizations or statistical models you've developed in R available to business analysts, marketers, managers or other non-programming types?
At the Journey to ROI event hosted by Accenture Analytics in San Francisco last week, four analytics executives held a panel discussion on how organizations can derive ROI from their analytics operations.
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.
I was recently looking through upcoming Coursera offerings and came across the course Coding the Matrix: Linear Algebra through Computer Science Applications taught by Philip Klein from Brown University. This looks like a fine course; but why use Python to teach linear algebra? I suppose this is a blind spot of mine: MATLAB I can see. That software has a long tradition of being used in applied mathematics and engineering applications.
Big Data is driving immediate changes in the insurance industry that will have long term effects well beyond its industry impact. Reacting to pressure from competitors and shareholders, insurance companies around the world are working to improve their analytical capabilities to deliver innovative and personalized products to both acquire new customers while ensuring the profitability of all their customers remains high.
Statistical forecasting is a critical component of every modern business, and Rob J Hyndman, Professor of Statistics at Monash University, is an expert in the field. He's the co-author of several books on forecasting, including Forecasting: Principles and Practice, a free on-line book that provides a comprehensive introduction to forecasting methods.
The world may indeed be awash with data, however, it is not always easy to find a suitable data set when you need one. As the number of people becoming involved with R and data science increases so does the need for interesting data sets for creating examples, showcasing machine learning algorithms and developing statistical analyses.
Today, Teradata announced the new Teradata Database 14.10 and with it some exciting news for R programmers: the first next-generation in-database R analytics that are fully parallel and scalable.
This guest post is by Punit Kulkarni. Punit is the Director of Marketing at Symphony Analytics and a marketing technology enthusiast. He has helped Fortune 500 retailers and brands in building their customer loyalty programs, direct marketing and business analytics.
In an overview of several predictive analytics platforms (including SPSS, Oracle and SAS), Butler Analytics offers this 4.5/5 star review of Revolution R Enterprise.
Early on, Revolution Analytics realized that R is more than just a tool for statistical computing — it is also the culture that has grown up around the use of the tool. The R culture is open and inclusive, competitive but also nourishing. There is a strong sense of community that encourages contribution and growth. We very much value R’s culture and community and we are doing our best to contribute.