Online or onsite, instructor-led live SAS training courses demonstrate through interactive hands-on practice how to automate repetitive processes, manage big data sets, etc with SAS.
SAS training is available as "online live training" or "onsite live training". Online live training (aka "remote live training") is carried out by way of an interactive, remote desktop. Onsite live SAS training can be carried out locally on customer premises in SuZhou or in NobleProg corporate training centers in SuZhou.
Situated right in the core of the Suzhou industry zone, The Regus Centre is on the 11th floor of Jinghope Plaza. Jinghope Pla...
Situated right in the core of the Suzhou industry zone, The Regus Centre is on the 11th floor of Jinghope Plaza. Jinghope Plaza is a mixed use complex comprising a high-end shopping mall, 5 star hotel and international Grade A office building. It is one of the most prestigious landmarks in the financial center of Suzhou. Advanced facilities and central location attract many Fortune 500 companies such as Nokia, Philips and 3M. Jinghope Plaza has easy access to excellent facilities and amenities in Suzhou. Metro Line 1 Times Square station is just in the basement of the complex. The high-speed train station which provides easy transportation to Shanghai and Nanjing is only a 5 minutes drive from the centre.
This instructor-led, live training in SuZhou (online or onsite) is aimed at data analysts who wish to program with R in SAS for cluster analysis.
By the end of this training, participants will be able to:
Use cluster analysis for data mining
Master R syntax for clustering solutions.
Implement hierarchical and non-hierarchical clustering.
Make data-driven decisions to help to improve business operations.
This instructor-led, live training in SuZhou (online or onsite) is aimed at data analysts who wish to program in SAS for advanced data management, data analysis, and more.
By the end of this training, participants will be able to:
Master macros to write efficient SAS programs.
Train a model and make predictions on unseen data with predictive modeling.
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