SAS Viya

Title Level Training Formats
Machine Learning Leadership and Practice – End-to-End Mastery Business Knowledge Series
  • Accessible to business learners and yet vital to techies as well

  • A vendor-neutral, universally applicable curriculum

  • Equivalent to a full-semester MBA or graduate-level course

Machine learning is booming. It reinvents industries and runs the world. According to Harvard Business Review, machine learning – also known as predictive analytics – is “the most important general-purpose technology of our era.”

But while there are so many how-to courses for hands-on techies, there are practically none that also serve the business leadership of machine learning. This is a striking omission since success with machine learning relies on a very particular project leadership practice just as much as it relies on adept number crunching. Without that leadership, most machine learning projects fail.

By filling that gap, this course empowers you to generate value with machine learning, whether you are a techie, a business leader, or some combination of the two. It delivers the end-to-end expertise that you need, covering both the core technology and the business-side practice.

Why cover both sides? Because both sides need to learn both sides! Everyone leading or participating in the deployment of machine learning must study them both.

Beyond the core tech. As with most machine learning courses, you'll learn how the technical methods work “under the hood” – in an accessible way that's understandable to all learners. But you'll also continue beyond that to master critical business-side best practices that are usually omitted.

1 Beginner e-Learning
SAS Viya Administration: Getting Started Free e-learning
This course provides an overview of administering SAS Viya.

The self-study e-learning includes:

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

1 Beginner e-Learning
SAS Viya Overview
This course provides an introduction to the applications in SAS Viya and discusses how each application is used in each phase of the SAS Analytics Life Cycle: Data, Discovery, Deployment, and Orchestration.

1 Beginner Classroom Live Web Classroom e-Learning
Building SAS Studio Flows in SAS Viya
This course is for users who do not have SAS programming experience but need to access and prepare data, and present summarized results. This course focuses on using flows, a point-and-click tool in SAS Studio that visualizes data transformation processes.

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

2 Fundamental Classroom Live Web Classroom e-Learning
System Tuning Using SAS Enterprise Session Monitor
This course is a one-day introduction to SAS Enterprise Session Monitor. SAS Enterprise Session Monitor brings visibility to complex analytic workloads, enabling SAS administrators and developers to keep business-critical SAS servers and applications stable and performing well, control cloud costs, and enable optimal efficiency at scale.

The self-study e-learning includes:

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

2 Fundamental Classroom Live Web Classroom e-Learning
SAS Viya Operations
This course introduces SAS administrators to SAS Viya 2021. This course provides an overview of the new SAS Viya architecture and shows how to perform essential SAS administration tasks like backups, monitoring, and logging using SAS Environment Manager, the command line interface, Kubernetes, and other open-source applications.

The self-study e-learning includes:

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

2 Fundamental Classroom Live Web Classroom e-Learning
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 Classroom 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 Classroom Live Web Classroom
Using SAS Viya REST APIs with Python and R
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.

3 Intermediate e-Learning
Risk Modeling Add-on for SAS Visual Machine Learning: Using Credit Scoring Nodes
This course provides an overview of the Risk Modeling nodes in SAS Visual Data Mining and Machine Learning. It prepares members of your organization to develop scorecards. The course includes hands-on demonstrations and teaches you key concepts, terminology, and base functionality that are integral to visual risk modeling.

3 Intermediate e-Learning
Large-Scale Forecasting Using SAS Viya: A Programming Approach
This course teaches students to develop and maintain a large-scale forecasting project using SAS Visual Forecasting tools. For the course project, students build and then refine a large-scale forecasting system. Emphasis is initially on selecting appropriate methods for data creation and variable transformations, model generation, and model selection. Students are then asked to improve overall baseline forecasting performance by modifying default processes in the system.

3 Intermediate 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 Classroom Live Web Classroom e-Learning
Machine Learning Using SAS Viya
This course discusses the theoretical foundation for techniques associated with supervised machine learning models. A series of demonstrations and practices 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 and deployment. 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.

3 Intermediate Classroom 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.

The classroom and Live Web course is appropriate for users of SAS Visual Analytics in SAS Viya 3.5. In e-learning, there is a course for users of SAS Visual Analytics in SAS Viya 3.5, and there is also a course for users of SAS Visual Analytics in SAS Viya 2020.1.

3 Intermediate Classroom 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 Classroom Live Web Classroom e-Learning
Neural Networks: Essentials
This course combines theory and practice to immerse you in the core concepts of neural network models and the essential practices of real-world application. During the course, you programmatically build a neural network and discover how to adjust the model’s essential parameters to solve different types of business challenges. You implement early stopping, build autoencoders for a predictive model, and perform an intelligent automatic search of the model hyperparameter values. The last lesson introduces deep learning. You gain hands-on practice building neural networks in SAS 9.4 and the cutting-edge, cloud-enabled in-memory analytics engine for big data analytics, SAS Viya.

The self-study e-learning includes:

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

3 Intermediate Classroom 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 Classroom Live Web Classroom e-Learning
SAS Viya Administration: Fast Track (3.5)
This course is a four-day immersion into a SAS Viya 3.5 environment. This course provides essential topics for SAS Viya 3.5 administration and demonstrates how to perform administration tasks using SAS Environment Manager, the command-line interface, and REST APIs.

The self-study e-learning includes:

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

3 Intermediate Classroom Live Web Classroom e-Learning
Network Analysis and Network Optimization in SAS Viya
This course provides a set of network analysis (graph theory) and network optimization solutions using the NETWORK and OPTNETWORK procedures in SAS Viya. Real-world applications are emphasized for each algorithm introduced in this course, including using network analysis as a stand-alone unsupervised learning technique, as well as incorporating network analysis and optimization to augment supervised learning techniques to improve machine learning model performance through input/feature creation.

The self-study e-learning includes:

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

3 Intermediate Classroom Live Web Classroom e-Learning
SAS Infrastructure for Risk Management: Solution Overview
This course introduces the user to SAS Infrastructure for Risk Management. It covers topics such as job flow instances, job flows, and federated areas.

The self-study e-learning includes:

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

3 Intermediate Classroom Live Web Classroom e-Learning
Working with SAS Viya Jobs
This course provides information about how to create, design, manage, and run SAS Viya jobs from SAS Studio.
This e-learning course includes virtual lab time to practice.

3 Intermediate Classroom Live Web Classroom e-Learning
High-Performance Data Processing with CASL in SAS Viya
In this course, you learn how to leverage SAS Cloud Analytics Services (CAS), the high-performance, in-memory analytics and distributed computing engine in SAS Viya. You learn how to access, explore, prepare, analyze, and summarize data using the native CAS programming language (CASL). CASL is a new scripting language designed to support the entire analytics life cycle.

3 Intermediate Classroom 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 Classroom Live Web Classroom e-Learning
SAS Viya Administration
This course is an introduction to the SAS Viya environment. This course provides essential topics for SAS Viya administration and demonstrates how to perform administration tasks using SAS Environment Manager, the command-line interface.

The self-study e-learning includes:

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

3 Intermediate Classroom Live Web Classroom e-Learning
SAS Viya Administration Fast Track
This course is a four-day immersion into a SAS Viya environment. This course provides essential topics for SAS Viya administration and demonstrates how to perform administration tasks using SAS Environment Manager, the command-line interface, and basic Kubernetes commands to administer a SAS Viya Environment.

The self-study e-learning includes:

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

3 Intermediate Classroom Live Web Classroom e-Learning
SAS Health Cohort Builder
This course explores the features and functionality specific to SAS Health: Cohort Builder in SAS Viya 4. Students gain competence in identifying patient cohorts of interest using simple or compound expressions, augmenting the patient cohort data with analysis variables, and leveraging templated analytics to gain knowledge and insights.

3 Intermediate Classroom Live Web Classroom e-Learning
Developing Custom Steps with SAS Studio Analyst
This course teaches you how to develop custom steps to create a user interface on top of your own code that can be shared, reused, and executed in SAS Studio flows.

3 Intermediate Classroom Live Web Classroom e-Learning
SAS Viya and Python Integration Fundamentals
In this course, you learn to use the SWAT (SAS Wrapper for Analytics Transfer) package to take advantage of the SAS Cloud Analytic Services (CAS) engine in SAS Viya for massively parallel processing (MPP) using familiar Python syntax. You learn about SAS Viya and the CAS engine, how to leverage the strengths of the CAS engine and your local Python client, how to connect Python to CAS, and how to access and load data into CAS's MPP environment. You then learn to explore, analyze, and prepare the data on the CAS server, taking advantage of the distributed processing power using familiar Pandas API and CAS actions from the SWAT package. Lastly, you learn how to return summarized results from the CAS server to your local Python client for additional processing and visualization using native Python packages.

3 Intermediate Classroom Live Web Classroom e-Learning
High-Performance Data Manipulation with SAS DS2
This course focuses on learning DS2, a fourth-generation SAS language for advanced data manipulation. DS2 blends DATA step and SQL syntax with modern programming structures, and enables parallel processing in Base SAS as well as massively parallel processing environments such as Hadoop and SAS Viya Cloud Analytic Services (CAS). A brief introduction to SAS Viya is included.

4 Expert Classroom Live Web Classroom
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 Classroom Live Web Classroom e-Learning
Regression Methods Using SAS Viya
This course covers nine regression methods. The models include linear, logistic, quantile, generalized linear, generalized additive, mixed, survival, nonlinear, and partial least squares. The applications, strengths, and weaknesses of each method are discussed, along with how the methods are implemented in SAS Viya. A comparison of the SAS Viya procedures and SAS/STAT procedures for each method is also shown. Examples in the course show applications in banking, financial services, direct marketing, insurance, telecommunications, medical, and academic fields.

The self-study e-learning includes:

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

4 Expert Classroom Live Web Classroom e-Learning