343 search results for "hadoop"

Revolution Newsletter: January 2013

January 23, 2013
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The most recent edition of the Revolution Newsletter is out. The news section is below, and you can read the full January edition (with highlights from this blog and community events) online. You can subscribe to the Revolution Newsletter to get it monthly via email. Top Innovator, Big Data Technologies. Revolution Analytics is the proud recipient of the Top...

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In case you missed it: December 2012 Roundup

January 17, 2013
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In case you missed them, here are some articles from December of particular interest to R users. The blog is.R ran an excellent series of R tips and applications in December, with posts including working with Stata files, working with graphs and networks, and text analysis. Kohske Takahashi provides R scripts to create a collection of optical illusions. Highlights...

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Software engineer’s guide to getting started with data science

December 30, 2012
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Software engineer’s guide to getting started with data science

Many of my software engineer friends ask me about learning data science. There are many articles on this subject from renowned data scientists (Dataspora, Gigaom, Quora, Hilary Mason). This post captures my journey (a software engin...

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Oracle R Enterprise 1.3 released

December 26, 2012
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We're pleased to announce the latest release of Oracle R Enterprise, now available for download. Oracle R Enterprise 1.3 features new predictive analytics interfaces for in-database model building and scoring, support for in-database sampling and partitioning techniques, and transparent support for Oracle DATE and TIMESTAMP data types to facilitate data preparation for time series analysis and forecasting. Oracle...

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Data Science, Data Analysis, R and Python

The October 2012 issue of Harvard Business Review prominently features the words “Getting Control of Big Data” on the cover, and the magazine includes these three related articles:“Big Data: The Management Revolution,” by Andrew McAfee and Erik Brynjolfsson, pages 61 – 68;“Data Scientist: The Sexiest Job of the 21st Century,” by Thomas H. Davenport and D.J. Patil, pages...

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Revolution Newsletter: December 2012

December 14, 2012
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The most recent edition of the Revolution Newsletter is out. The news section is below, and you can read the full December edition (with highlights from this blog and community events) online. You can subscribe to the Revolution Newsletter to get it monthly via email. Tell us what you're looking for in R training. 2013 is the International Year...

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In case you missed it: November 2012 Roundup

December 12, 2012
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In case you missed them, here are some articles from November of particular interest to R users. In the webinar "Real-Time Predictive Analytics with Big Data", I showed how R fits into a real-time production system. R package developer Yihui Xie shares his favorite software and hardware in an interview with The Setup. Hadley Wickham created a handy tutorial...

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Four years of the Revolutions Blog

December 10, 2012
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Yesterday was the fourth anniversary of the Revolutions blog. Our first post was way back on December 9, 2008, and in the four years since we've been regularly posting about R, open source, statistics, big data, data science and other random things that happened to catch our eye. In fact, there have been 1488 posts published in the last...

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Please stop using Excel-like formats to exchange data

December 7, 2012
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Please stop using Excel-like formats to exchange data

I know “officially” data scientists all always work in “big data” environments with data in a remote database, streaming store or key-value system. But in day to day work Excel files and Excel export files get used a lot and cause a disproportionate amount of pain. I would like to make a plea to my Related posts:

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Importing Data Into R from Different Sources

December 6, 2012
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I have found that I get data from many different sources.  These sources range from simple .csv files to more complex relational databases, to structure XML or JSON files.  I have compiled the different approaches that one can use to easily access these datasets. Local Column Delimited Files This is probably the most common and

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