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Data Mining Wisdom on the Web

Posted by: Keith McCormick May 8, 2014 8:20:00 AM
At QueBIT we like to describe Advanced Analytics as the powerful combination of Predictive Analytics and Prescriptive Analytics. The term Data Mining is still in wide use, however. It can be... Read More

Keith McCormick

Keith McCormick is the Vice President and General Manager of QueBIT’s Advanced Analytics team. He brings a wealth of consulting/training experience in statistics, predictive modeling and analytics, and data mining. For many years, he has worked in the SPSS community, first as an External Trainer and Consultant for SPSS Inc., then in a similar role with IBM, and now in his role with an award winning IBM partner. He possesses a BS in Computer Science and Psychology from Worcester Polytechnic Institute. He has been using Stats software tools since the early 90s, and has been training since 1997. He has been doing data mining and using IBM SPSS Modeler since its arrival in North America in the late 90s. He is an expert in IBM’s SPSS software suite including IBM SPSS Statistics, IBM SPSS Modeler (formally Clementine), AMOS, Text Mining, and Classification Trees. He is active as a moderator and participant in statistics groups online including LinkedIn’s Statistics and Analytics Consultants Groups. He also blogs and reviews related books at KeithMcCormick.com. He enjoys hiking in out of the way places, finding unusual souvenirs while traveling overseas, exotic foods, and old books. Author, with Scott Mutchler and others, of the IBM SPSS Modeler Cookbook.
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It’s not SPSS Modeler or R – it’s SPSS Modeler AND R

Posted by Keith McCormick

The R programming language is the result of a collaborative effort with contributions from all over the world. Initially written by Robert Gentleman and Ross Ihaka of the University of Auckland in the late 90s, it is popular and in wide use. It has been featured in the New York Times. Even estimates that are several years old have put the number of users above a ¼ million. The current number is certainly much higher. One popular LinkedIn group has 30,000 members. It has been featured in the New York Times. Polls on KDNuggets.com have placed its popularity even higher than the two players that have dominated statistical computing for decades: SPSS Statistics and SAS. The open source nature, and its corresponding price, are extremely attractive to academics and students. Critically, it is also very powerful.

So what’s the catch? Even its fans admit to a learning curve. It is a programming language, so there is no Graphical User Interface to get you quickly up to speed. Software environments have been created to support working in R, and many of them are popular, but nonetheless, there is some effort to be spent on getting started. On the upside, it is universally recognized as having fine graphics capability and if measured solely in terms of sheer volume, no commercial package can compete with the number of algorithms and methods available in R.

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Topics: Modeler

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