Learn how to maximize your data with SAS and Hadoop

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California or bust

California or bust

Outside, the Cary, NC sky is gray and winds are blowing freezing rain, but a group of statisticians at SAS are channeling warm green hills and the soft, gold light of a California evening. Team conversations alternate between distributed processing, PROC IMSTAT and how many pairs of shorts to pack.

For the past several months, the Advanced Analytics training team here in Cary have been hard at work developing a course especially for the Strata+ Hadoop World conference entitled Machine Learning and Exploratory Modeling with SAS® and Hadoop. I’m very excited about this unique course. It blends many topics, and focuses exclusively on enhancing and refining students’ analytic skills in Hadoop.

The course will be held in San Jose, CA Feb 17-18 and was created for analytic professionals who want to make the most of their big data with Hadoop and SAS by incorporating high-performance, machine learning algorithms with predictive modeling best practices.

On the first day, we’ll primarily spend time using SAS Visual Analytics and Visual Statistics to perform analyses using the point-and-click interface. Because there will always be a need to do more than you see in the GUI, the second day is devoted to using PROC IMSTAT and High-Performance procedures for predictive modeling and text analytics, and the RECOMMEND procedure to build a recommendation system.

Featuring this course at the Strata conference is the perfect fit and a great value for analytic professionals. Your course registration fee includes a 2-day Expo Hall pass. This gives you the opportunity to network with data science professionals from around the world, who are experienced in many different technologies.  Good news! We are offering a special 30 percent discount to SAS customers. To take advantage of the discount, register using the promo code SASML. I’d love to see you there.

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About Author

Catherine Truxillo

Catherine Truxillo, Ph.D. has been a Statistical Training Specialist at SAS since 2000 and has written or co-written SAS training courses for advanced statistical methods including: multivariate statistics, linear and generalized linear mixed models, multilevel models, structural equation models, imputation methods for missing data, statistical process control, design and analysis of experiments, and cluster analysis. Although she primarily works with advanced statistics topics, she also teaches SAS courses using SAS/IML (the interactive matrix language), SAS Enterprise Guide, SAS Enterprise Miner, SAS Forecast Studio, and JMP software. Before coming to SAS, Catherine completed her Ph.D. in Social Psychology with an emphasis in Statistics at The University of Texas at Austin. While at UT Austin, she completed an internship with the Math and Computer Science department's statistical consulting help desk and taught a number of undergraduate courses. While teaching and performing her own graduate research, she worked for a software usability design company conducting experiments to assess the ease-of-use of various software interfaces and website designs. Cat's personal interests include triathlon, hiking the woods near her home in North Carolina, and having tea parties with her two children.

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