SAS Visual Statistics

Title Level Training Formats
Strategies and Concepts for Data Scientists and Business Analysts Business Knowledge Series
To be effective in a competitive business environment, analytics professionals need to use descriptive, predictive, and prescriptive analytics to translate information into decisions. An effective analyst also should be able to identify the analytical tools and data structures to anticipate market trends.

In this course, you gain the skills data scientists and statistical business analysts must have to succeed in today's data-driven economy. Learn about visualizing big data, how predictive modeling can help you find hidden nuggets, the importance of experiments in business, and the kind of value you can gain from unstructured data.

This course combines scheduled, instructor-led classroom or Live Web sessions with small-group discussion, readings, and hands-on software demonstrations, for a highly engaging learning experience.

3 Intermediate Classroom Live Web Classroom
Introduction to Data Science
The goal of this course is to make analytics approachable and comprehensible. After completing this course, you will be able to perform the main data-related tasks for the citizen data scientist using the point-and-click capabilities of SAS Visual Analytics and SAS Visual Statistics: accessing and manipulating data, exploring data using analytics, and building predictive models.

1 Beginner Live Web Classroom
Summer Fast Track: Accessible Analytics
This course is for those who are working with big data and want to prepare their data, explore their data, and model their data in a visual way. Results will be presented in an interactive report. SAS Visual Analytics and SAS Visual Statistics are the tools that are used in the training. This fast track combines the courses SAS Visual Analytics 1 for SAS Viya: Basics, SAS Visual Analytics 2 for SAS Viya: Advanced, and SAS Visual Statistics in SAS Viya: Interactive Model Building.

1 Beginner Live Web Classroom
SAS Viya Overview
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
SAS Visual Statistics: 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 7.2, 7.3, and 7.4.

3 Intermediate Classroom
Data Mining Techniques: Predictive Analytics on Big Data
This course introduces applications and techniques for assaying and modeling large data. The course also presents basic and advanced modeling strategies, such as group-by processing for linear models, random forests, generalized linear models, and mixture distribution models. Students perform hands-on exploration and analyses using tools such as SAS Enterprise Miner, SAS Visual Statistics, and SAS In-Memory Statistics.

3 Intermediate Classroom Live Web Classroom e-Learning
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.

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 Classroom Live Web Classroom e-Learning
Advanced Machine Learning Using SAS Viya
This course teaches you how to optimize the performance of predictive models beyond the basics by implementing various data munging and wrangling techniques. The course continues the development of supervised learning models that begins in the Machine Learning Using SAS Viya course and extends it to ensemble modeling. Running unsupervised learning and semi-supervised learning models is also discussed. In this course, you learn how to do feature engineering and clustering of variables, and how to preprocess nominal variables and detect anomalies. This course uses Model Studio, the pipeline flow interface in SAS Viya that enables you to prepare, develop, compare, and deploy advanced analytics models. Importing and running external models in Model Studio is also discussed, including open-source models. SAS Viya automation capabilities at each level of machine learning are also demonstrated, followed by some tips and tricks with Model Studio.

The self-study e-learning includes:

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

4 Expert Classroom Live Web Classroom e-Learning