SAS Viya is an in-memory distributed environment used to analyze big data quickly and efficiently. In this course, you’ll learn how to use the SAS Viya APIs to take control of SAS Cloud Analytic Services from a Jupyter Notebook using R or Python. You’ll learn to upload data into the cloud, analyze data, and create predictive models with SAS Viya using familiar open source functionality via the SWAT package -- the SAS Scripting Wrapper for Analytics Transfer. You’ll learn how to create both machine learning and deep learning models to tackle a variety of data sets and complex problems. And once SAS Viya has done the heavy lifting, you’ll be able to download data to the client and use native open source syntax to compare results and create graphics.
In this module, you meet the instructor and learn about course logistics, such as how to access the software for this course.
SAS® Viya® and Open Source Integration
In this module you learn about the analytical processing engine behind SAS Viya, the Cloud Analytic Services server. You also learn how to submit data processing commands to SAS Viya from the open source languages R and Python.
In this module you learn how to use R and Python to create, optimize, and assess SAS Viya predictive models. You also learn how to use R and Python to efficiently manage the creation and assessment of these models.
In this module you learn how natural language processing is used to analyze collections of text documents. You also learn how to turn blocks of unstructured text into numeric inputs suitable for predictive modeling.
In this module you learn how deep learning methods extend traditional neural network models with new options and architectures. You also learn how recurrent neural networks are used to model sequence data like time series and text strings, and how to create these models using R and Python APIs for SAS Viya.
In this module you learn how to model time series using two popular methods, exponential smoothing and ARIMAX. You also learn how to use the R and Python APIs for SAS Viya to create forecasts using these classical methods and using recurrent neural networks for more complex problems.
In this module you learn how convolutional neural networks are used to classify images and how to use the R and Python APIs for SAS Viya to create convolutional neural networks.
In this module you learn how factorization machines are used to create recommendation engines and how to build factorization machine models in SAS Viya using the R and Python APIs.