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 Integration
Cloud Analytic Services
Open Source Development Interfaces
Scripting Wrapper for Analytics Transfer (SWAT)
Fundamentals of the R and Python APIs
Machine Learning
Predictive Modeling
Predictive Models
Model Assessment
Text Analytics
Introduction to Text Analytics
Natural Language Processing
Deep Learning
Traditional Neural Networks versus Deep Learning
Recurrent Neural Networks
Time Series
Time Series Modeling and Forecasting
Exponential Smoothing Models
ARIMAX Models
Image Classification
Deep Learning Image Classification
Factorization Machines
Modeling Interactions in Factorization Machines
The hands-on lab is preconfigured to support this course and will not support hands-on practice for all your enrolled courses.
Hands-On Lab Reservation System
When you are planning your study time, keep in mind that the virtual lab takes 60-75 minutes to start
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