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SAS Viya

Title Level Training Formats
SAS Viya Enablement Free
This course highlights SAS Viya, our new open platform built for analytics innovation. The course covers SAS Viya Administration, Transitioning from SAS 9 to SAS Viya, Data Mining and Machine Learning, SAS Visual Analytics 8.1, SAS Visual Investigator, SAS Econometrics, SAS Optimization, and SAS Visual Forecasting. The course also provides access to two workshops on SAS Viya tools.

0 No level e-Learning
SAS Visual Investigator: Building the Interface
This course provides an introduction to SAS Visual Investigator 10.2 and teaches you how to develop an application interface.

2 Fundamental 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 Intermediate Classroom Live Web Classroom
Programming for SAS Viya New
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.

0 No level Classroom Live Web 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.

0 No level Classroom Live Web Classroom
Supervised Machine Learning Pipelines Using SAS Viya New
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.

3 Intermediate Classroom
SAS Visual Statistics on 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 on SAS Viya 8.2.

3 Intermediate Classroom Live Web Classroom
Neural Networks: Essentials New
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 Live Web Classroom