SAS Viya

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SAS Viya Enablement Free e-learning
The SAS Viya Enablement training provides access to information about SAS Viya and the related products. This free e-learning provides access to a collection of information, including videos and documentation as well as links to additional product information. This is not a traditional e-learning course.

1 Beginner e-Learning
SAS Visual Investigator: Building the Interface
This course teaches you how to develop an application interface for monitoring financial crimes.

2 Fundamental Live Web Classroom e-Learning
The Magic of Compelling Reports and Visualizations with SAS

Whether you’re a SAS programmer or a SAS Visual Analytics user, we’ve hand-picked a range of topics to advance your report development and data visualisation skills. With three conference streams to choose from and sessions delivered by our expert trainers, you’ll also have the chance to get hands-on experience, gain extended access to your own SAS environment and try out the techniques you’ve seen.


Register soon and make advantage of the early bird discount of 30% which is valid until 9 October. Please use the Promotion Code LC2020.


0 No level Live Web Classroom
SAS Viya: Overview for Beginners and SAS9 Users
Are you already using elements of SAS 9 and would like to know what innovations and advantages SAS Viya offers?

You know SAS Viya from hearsay and want to get an understanding of the usage and architecture of this modern, cloud-enabled part of the SAS Platform?

This course is aimed at users who want to gain insight into the SAS Viya world in a short time. During the course, you learn the elements of the SAS Viya architecture and SAS Viya applications, create interactive analyses and reports, and get to know the new way of programming with the CAS language. At the appropriate points, the interaction between SAS 9 and SAS Viya, as well as the differences, are discussed.

2 Fundamental Live Web Classroom
SAS Viya and Python Integration for Machine Learning
In this course, you learn to use the Python API to take control of SAS Cloud Analytic Services (CAS) actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models in CAS using familiar Python functionality via the SWAT (SAS Wrapper for Analytics Transfer) package. You then learn to download results to the client and use native Python syntax to compare models.

3 Intermediate Live Web Classroom
SAS® Viya® and R Integration for Machine Learning
In this course, you learn to use the R API to take control of SAS Cloud Analytic Services (CAS) actions from Jupyter Notebook. You learn to upload data into the in-memory distributed environment, analyze data, and create predictive models in CAS using familiar R functionality via the SWAT (SAS Wrapper for Analytics Transfer) package. You then learn to download results to the client and use native R syntax to compare models.

3 Intermediate Live Web Classroom
SAS Risk Modeling: Using the Solution
SAS Risk Modeling enables you to quickly and efficiently create analytical base tables that are used for developing credit scoring models. In this course, you learn how to create analytical base tables by calculating variables from different sources. Using SAS Model Studio to develop an application scorecard is demonstrated and practiced. By the end of this course, you will be comfortable working in Risk Modeling workspaces that are used for implementing models and monitoring their performance.

3 Intermediate e-Learning
Neural Networks: Essentials
This course covers the essential components of a neural network architecture and details how to modify the structure as needed. Modifying the neural network’s error functions, activation functions, and hidden units is discussed. You learn how to programmatically build a neural network, implement early stopping, build autoencoders for a predictive model, and perform an intelligent search of the model hyperparameter values. The course finishes with an introduction to deep learning. The NEURAL procedure from SAS 9.4 and the NNET procedure from SAS Viya are used throughout the course.

3 Intermediate Classroom e-Learning
Self-Service Data Preparation in SAS Viya
This course provides an overview of the analytic data preparation capabilities of SAS Data Preparation in SAS Viya. These self-service data preparation capabilities include bringing data in from a variety of sources, preparing and cleansing the data to be fit for purpose, analyzing data for better understanding and governance, and sharing the data with others to promote collaboration and operational use.

3 Intermediate Live Web Classroom e-Learning
Programming for SAS Viya
This course is for users who need to modify existing Base SAS programs that will execute in SAS Viya. This course leverages the power of SAS Cloud Analytic Services (CAS) to access, manage, and manipulate in-memory tables. This course is not intended for beginning SAS software users.

3 Intermediate Live Web Classroom e-Learning
SAS Visual Statistics in SAS Viya: Interactive Model Building
This course introduces SAS Visual Statistics for building predictive models in an interactive, exploratory way. Exploratory model fitting is a critical step in modeling big data. This course is appropriate for users of SAS Visual Analytics in SAS Viya 3.5.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual Lab time to practice.

3 Intermediate Live Web Classroom e-Learning
Deep Learning Using SAS Software
This course introduces the pivotal components of deep learning. You learn how to build deep feedforward, convolutional, recurrent networks, and variants of denoising autoencoders. The neural networks are used to solve problems that include traditional classification, image classification, and sequence-dependent outcomes. The course contains a healthy mix of theory and application. Hands-on demonstration and practice problems are included to reinforce key concepts. Hyperparameter search methods are described and demonstrated to find an optimal set of deep learning models. Transfer learning is covered because the emergence of this field has shown promise in deep learning. Lastly, you learn how to customize a SAS deep learning model to research new areas of deep learning.

3 Intermediate Live Web Classroom e-Learning
Supervised Machine Learning Procedures Using SAS Viya in SAS Studio
This course covers a variety of machine learning techniques that are performed in a scalable and in-memory execution environment. The course provides hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The machine learning techniques include logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, factorization machine, and Bayesian networks.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.

3 Intermediate Live Web Classroom e-Learning
Supervised Machine Learning Pipelines Using SAS Viya
This course covers the theoretical foundation for different techniques associated with supervised machine learning models. A series of demonstrations and exercises is used to reinforce all the concepts and the analytical approach to solving business problems. In addition, a business case study is defined to guide participants through all steps of the analytical life cycle, from problem understanding to model deployment, through data preparation, feature selection, model training and validation, and model assessment. This course is the core of the SAS Viya Data Mining and Machine Learning curriculum. It uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. You learn to train supervised machine learning models to make better decisions on big data. The SAS applications used in this course make machine learning possible without programming or coding.


The e-learning version of this course provides access to SAS Viya for Learners, which enables students to use the software to complete the practices.

3 Intermediate Classroom Live Web Classroom e-Learning
Tree-Based Machine Learning Methods in SAS Viya
Decision trees and tree-based ensembles are supervised learning models used for problems involving classification and regression. This course covers everything from using a single tree to more advanced bagging and boosting ensemble methods in SAS Viya. The course includes discussions of tree-structured predictive models and the methodology for growing, pruning, and assessing decision trees, forest and gradient boosting models. The course also explains isolation forest (an unsupervised learning algorithm for anomaly detection), deep forest (an alternative for neural network deep learning), and Poisson and Tweedy gradient boosted regression trees. In addition, many of the auxiliary uses of trees, such as exploratory data analysis, dimension reduction, and missing value imputation, are examined, and running open source in SAS and running SAS in open source are demonstrated.

The self-study e-learning includes:

  • Annotatable course notes in PDF format.
  • Virtual lab time to practice.

4 Expert e-Learning