SAS Viya

名称 水平 培训形式
SAS Viya 系列培训
本课程将从大数据分析的系统架构、数据管理、数据可视化探索、可视化建模分析、可视化机器学习和深度学习等方面介绍全新一代的数据分析平台SAS Viya。

0 No level Classroom
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
FDP-Shaping a Data Science Curriculum: Data Management, Machine Learning and Artificial Intelligence
This FDP supports developing a data science program that covers a variety of topics and enables students to acquire the skills that industry is looking for their employees to have. The FDP helps universities develop a pool of talent with the range of analytical and technology skills to work in a data-rich business environment.

1 Beginner Live Web Classroom
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 Live Web Classroom
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 New 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 New
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 e-Learning
System Tuning Using SAS Enterprise Session Monitor New
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 e-Learning
SAS Viya 管理:增强任务
本课程探讨SAS管理员可能需要在SAS Viya环境中执行的正在进行的任务。 本课程概述了SAS Viya部署拓扑,并展示了如何使用SAS Environment Manager和命令行界面以及REST API执行正在进行的SAS管理任务。

自学式电子学习内容包括:

  • 可注释的PDF格式的课程笔记。
  • 虚拟实验室的练习时间。

3 Intermediate 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
Network Analysis and Network Optimization in SAS Viya New
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
SAS Infrastructure for Risk Management: Solution Overview New
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 e-Learning
SAS Viya Administration Self-Study (PDF + 20 virtual lab hours) New

3 Intermediate e-Learning
Working with SAS Viya Jobs New
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 e-Learning
Using SAS Viya REST APIs with Python and R New
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
High-Performance Data Processing with CASL in SAS Viya New
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 e-Learning
Risk Modeling Add-on for SAS Visual Machine Learning: Using Credit Scoring Nodes New
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
SAS Viya 中的自助数据准备
本课程概述了SAS Viya中SAS数据准备的分析数据准备功能。 这些自助数据准备功能包括从各种来源引入数据,准备和清理适合目的的数据,分析数据以更好地理解和治理以及与他人共享数据以促进协作和运营使用。

3 Intermediate Live Web Classroom e-Learning
使用SAS Viya的机器学习 New
本课程讨论与监督机器学习模型相关的技术的理论基础。 一系列的演示和实践用于强化解决业务问题的所有概念和分析方法。 此外,还定义了一个业务案例研究,以指导参与者完成分析生命周期的所有步骤,从问题理解到模型部署,再到数据准备,功能选择,模型训练和验证以及模型评估和部署。 本课程是SAS Viya数据挖掘和机器学习课程的核心。 它使用Model Studio(SAS Viya中的管道流接口),使您能够准备,开发,比较和部署高级分析模型。 您将学习训练有监督的机器学习模型,以便对大数据做出更好的决策。

3 Intermediate Live Web Classroom e-Learning
SAS Visual Statistics in SAS Viya: Interactive Model Building New
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 Live Web Classroom e-Learning
Deep Learning Using SAS Software New
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
SAS Viya Administration: Fast Track New
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 Live Web Classroom e-Learning
SAS Viya 编程
本课程适用于需要修改将在SAS Viya中执行的现有基本SAS程序的用户。 本课程利用SAS Cloud Analytic Services(CAS)的功能来访问,管理和操作内存中的表。 本课程不适合SAS新手用户使用。

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 Live Web Classroom
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 Live Web Classroom e-Learning