SAS Viya

Kurstitel Stufe Kursformat
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 Einsteiger e-Learning
SAS® Visual Investigator: Building the Interface
This course teaches you how to develop an application interface for monitoring financial crimes.

2 Basiswissen Classroom Live Web Classroom
Supervised Machine Learning Procedures Using SAS® Viya® in SAS® Studio
This course combines data exploration, visualization, data preparation, feature engineering, sampling and partitioning, model training, scoring, and assessment. It covers a variety of statistical, data mining, and machine learning techniques performed in a scalable and in-memory execution environment. The course provides theoretical foundation and hands-on experience with SAS Visual Data Mining and Machine Learning through SAS Studio, a user interface for SAS programming. The course includes predictive modeling techniques such as linear and logistic regression, decision tree and ensemble of trees (forest and gradient boosting), neural networks, support vector machine, and factorization machine.

3 Für Fortgeschrittene Classroom Live Web Classroom
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 8.3.

The self-study e-learning includes:

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

3 Für Fortgeschrittene Classroom Live Web Classroom e-Learning
Network Analysis and Network Optimization in SAS® Viya®
This course provides a set of algorithms to perform both network analysis and network optimization. Practical demonstrations and exercises emphasize the theory, and business case studies illustrate the possible outcomes from such techniques. Hands-on activities are based on the NETWORK and OPTNETWORK procedures in SAS Viya.

Network analysis includes graph theory algorithms that can augment data mining and machine learning. In many practical applications, pairwise interaction between the entities of interest in the model often plays an important role. Network analysis goes beyond traditional clustering and predictive models to identify patterns in business data, including entities’ behavior based on their relationships. Network analysis can be employed to avoid churn, diffuse products and services, detect fraud and abuse, identify anomalies, and many other applications, in a wide range of industries such as communications and media, banking, insurance, retail, utilities, and travel and transportation.

Network optimization includes graph theory algorithms that can augment more generic mathematical optimization approaches. Many practical applications of optimization depend on an underlying network. Networks also appear explicitly and implicitly in many other application contexts. Networks are often constructed from certain natural co-occurrence types of relationships, such as relationships among researchers who coauthor articles, actors who appear in the same movie, words or topics that occur in the same document, items that appear together in a shopping basket, terrorism suspects who travel together or are seen in the same location, and so on. In these types of relationships, the strength or frequency of interaction is modeled as weights on the links of the resulting network.

3 Für Fortgeschrittene Classroom
Data Preparation on the SAS® Viya® Open Analytic Platform
This course provides an overview of the analytic data preparation capabilities of SAS Data Preparation on the SAS Viya platform. 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 Für Fortgeschrittene 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 Für Fortgeschrittene 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 Für Fortgeschrittene Classroom Live Web Classroom
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.

3 Für Fortgeschrittene Classroom Live Web Classroom
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 Für Fortgeschrittene 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 Für Fortgeschrittene Classroom Live Web Classroom e-Learning
SAS® Viya® Administration: Enhanced Tasks
This course explores ongoing tasks that SAS administrators might need to perform in their SAS Viya environment. This course provides an overview of SAS Viya deployment topologies and shows how to perform ongoing SAS administration tasks using SAS Environment Manager and 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 Für Fortgeschrittene 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 in detail because of 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 Für Fortgeschrittene Classroom Live Web Classroom e-Learning
Machine Learning Using SAS® Viya®
This course discusses the theoretical foundation for different 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.

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 Für Fortgeschrittene Classroom Live Web Classroom e-Learning
SAS® Visual Analytics 2 for SAS® Viya®: Advanced

Unseren Präsenzkurs für SAS® Visual Analytics for Viya® finden Sie auf einer separaten Webseite.


This course describes advanced features of data preparation, analytics, and report creation in SAS Visual Analytics.

4 Für Experten Classroom Live Web Classroom e-Learning