In this course, you learn to use the R and Python APIs to take control of SAS Cloud Analytic Services (CAS) and submit actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models on CAS using familiar open-source functionality via the SWAT (SAS Wrapper for Analytics Transfer) package.
Learn How To
Use the R and Python APIs in SAS Viya.Submit CAS actions from Jupyter Notebook.Move data between the client and the server.Manage, alter, and prepare data on the CAS server.Create machine learning and deep learning models on the CAS server.Use open-source syntax to wrap up CAS actions with functions and loops.Who Should Attend
Data scientists with open source experience who want to take advantage of SAS Viya distributed analytics
Prerequisites
Students should have experience working with data, creating predictive models, and writing open-source programs. Some SAS experience is recommended.
SAS Products Covered
SAS Viya;SAS Visual Data Mining and Machine Learning
Course Outline
SAS Viya and Open Source Integration
SAS Approach to Open Source IntegrationCloud Analytic ServicesOpen Source Development InterfacesScripting Wrapper for Analytics Transfer (SWAT)Fundamentals of the R and Python APIsMachine LearningPredictive ModelingPredictive ModelsModel AssessmentText AnalyticsIntroduction to Text AnalyticsNatural Language ProcessingDeep LearningTraditional Neural Networks versus Deep LearningRecurrent Neural NetworksTime SeriesTime Series Modeling and ForecastingExponential Smoothing ModelsARIMAX ModelsImage ClassificationDeep Learning Image ClassificationFactorization MachinesModeling Interactions in Factorization Machines
