SAS Viya

Title Level Training Formats
SAS Viya Enablement Free e-learning
This course highlights SAS Viya, our open platform built for analytics innovation.

1 Beginner e-Learning
SAS Learning Conference - Do more with Programming & Analytics
We would like to invite you to a different way of learning SAS.

We do it in shorter time, covering more topics, lot's of hands-on exercises and get closer to your specific SAS-challenges.

Experts at Your Service
The instructors at the event are some of our most experienced colleagues in the Nordics, with many years of experience using SAS.

The learning day has two tracks and the possibility to Ask-the-experts.

In the first two we have shorter presentations of approximately one hour with handouts and hands-on exercises for selected sessions to underpin that you can bring the main points with you home. For the Ask-the-experts sessions, you can bring you own issues and get help on your specific questions.

This day is the ultimate possiblility for Tips & Tricks for Programming and Analytics.

0 No level Classroom
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
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.

3 Intermediate Classroom Live Web Classroom
Deep Learning Using SAS Software
This course introduces the essential components of deep learning. Participants learn how to build deep feedforward, convolutional, and recurrent networks. The neural networks are used to solve problems that include traditional classification, image classification, and time-dependent outcomes. The course also presents practical methods used to enhance training data to produce better models. Lastly, a method for efficiently searching hyperparameters is described.

3 Intermediate Classroom 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 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
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 Intermediate 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
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 computing platform for big data analytics, SAS Viya.

3 Intermediate 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 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.

The e-learning includes Virtual Lab time to practice.

3 Intermediate 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 Intermediate Classroom Live Web Classroom e-Learning