SAS Viya

Názov Úroveň Typ školenia
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 New
This course teaches you how to develop an application interface for monitoring financial crimes.

2 Fundamental Live Web Classroom e-Learning
SAS Visual Statistics on SAS Viya: Interactive Model Building
This course introduces SAS Visual Statistics on SAS Viya 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 8.1 who have licensed Visual Statistics.

3 Intermediate Classroom Live Web Classroom e-Learning
FDP - Shaping a Business Analytics Curriculum
This outline is provisional and subject to change.The Faculty Development Program (FDP) teaches you fundamental concepts and relevant techniques in self-service data preparation, business visualization, and modeling using a powerful mix of point-and-click visual SAS tools. The course also enables you to explore academic and collaborative opportunities with SAS in the area of advanced analytics for designing better curriculum and effective pedagogy.

0 No level Live Web Classroom
FDP - Shaping an Analytics and Machine Learning Curriculum
This outline is provisional and subject to change.The Faculty Development Program (FDP) teaches you fundamental concepts and relevant techniques in analytics and machine learning using a powerful mix of point-and-click visual SAS tools, including visual analytics, visual data mining and machine learning, and text analytics. The course also enables you to explore academic and collaborative opportunities with SAS in the area of advanced analytics for designing better curriculum and effective pedagogy.

0 No level Live Web Classroom
FDP - Shaping an Analytics Curriculum (A SAS & Open Source Approach)
This outline is provisional and subject to change.The Faculty Development Program (FDP) teaches you fundamental concepts and relevant techniques in analytics and machine learning using a powerful mix of SAS and open-source programming languages. The course also enables you to explore academic and collaborative opportunities with SAS in the area of advance analytics for designing better curriculum and effective pedagogy.

0 No level Live Web Classroom
SAS® Viya® :機械学習
本コースでは、教師あり機械学習モデルに関連するさまざまな手法の理論的基礎について説明します。
一連のデモンストレーションとプラクティスを使用して、ビジネス上の問題を解決するためのすべての概念と分析的アプローチを強化します。
さらに、ビジネス・ケーススタディは、参加者のために、問題の理解からモデルの展開、データの準備、機能の選択、モデルのトレーニングと検証、モデルの評価と展開まで、アナリティクス・ライフサイクルのすべてのステップを通して定義されています。

0 No level Live Web Classroom e-Learning
Self-Service Data Preparation in SAS Viya (Japanese)

0 No level 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.

0 No level Live Web Classroom e-Learning
SAS Platform Administration: Getting Started Free e-learning
This course provides an overview of SAS Viya and SAS 9.4, the SAS Platform.

1 Beginner e-Learning
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: 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 New
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
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 e-Learning
Risk Modeling Add-on for SAS Visual Machine Learning: Using Credit Scoring Nodes (multi-version course) (+10 virtual lab hours)
This course provides an overview of the Risk Modeling nodes in SAS Visual 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.

The e-learning course covers Risk Modeling Add-on for SAS Visual Machine Learning, including previous versions.

3 Intermediate e-Learning
Using SAS® Viya® REST APIs with Python and R (multi-version + virtual lab)
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.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.


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 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
Neural Networks: Essentials New
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 Live Web Classroom e-Learning
Supervised Machine Learning Procedures Using SAS Viya in SAS Studio New
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
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.

The e-learning course covers SAS Studio 5.1, 5.2, and 2020.1 in SAS Viya.

3 Intermediate 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 Live Web Classroom e-Learning
Tree-Based Machine Learning Methods in SAS Viya New
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