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R-core member Peter Dalgaard announced yesterday that R 3.0.3 is now available. This is the final update to the R 3.0 series, and includes several small but handy new features and minor bug fixes. Improvements include support for writing very large tables to disk, better handling of foreign-language calendar dates, and more accuracy when calculating extreme quantiles of the Cauchy distribution. 

Will we always need data scientists, or will the Data Science role be replaced by easy-to-use automated applications? Mikio L.

by Rodney Sparapani, PhD

Rodney is an Assistant Professor in the Institute for Health and Society from the Division of Biostatistics at the Medical College of Wisconsin in Milwaukee and president of the Milwaukee Chapter of the ASA which is hosting an R workshop on Data Mining in Milwaukee on April 4th.

Since we've had quite a few announcements over the last month or so, I thought I'd take a moment to catch up on some of the media reports mentioning Revolution Analytics.

Since we got some great news the other day, a happiness-filled Because it's Friday post is a must this week. This Pomplamoose remix of Pharrell William's Happy with Daft Punk's Get Lucky — featuring very clever use of a standard "beamer" video projector for the visual effects — fits the bill nicely.

 

This is the time of year when everyone likes to speculate on the winners of the Academy Awards, to be announced on Sunday. There are plenty of ways to try and predict which movie is going to win Best Picture or who'll win Best Actress. You could look at the various betting markets and see who the speculators are favouring. You could take a look at the predictions from various movie experts.

by Daniel Hanson, QA Data Scientist, Revolution Analytics

Introduction

As most readers are well aware, market return data tends to have heavier tails than that which can be captured by a normal distribution; furthermore, skewness will not be captured either. For this reason, a four parameter distribution such as the Generalized Lambda Distribution (GLD) can give us a more realistic representation of the behavior of market returns, and a source from which to draw random samples to simulate returns.