SAS Institute Inc.
SAS is the leader in analytics, from data science to AI and machine learning. Build skills to help you land some of today's most sought-after positions, such as data scientists and business analysts, with books developed and written by SAS experts.
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The SAS® Add-In for Microsoft Office: A Beginner's Guide
Created by SAS Institute Inc.
Learn the basics of the SAS Add-In for Microsoft Office to easily generate, customize, and share reports!
Tap into the power of SAS through familiar Microsoft applications to create meaningful reports and presentations to share with others. With the SAS Add-in for Microsoft Office, you can explore data or insert SAS tables and charts into a spreadsheet or document to work with them in Office – all with no programming skills required! This book will show you how to use the SAS Add-in in various applications and explain how to use the SAS Add-In’s dynamic features to create customized results with just a few clicks. Seamless integration makes it easy to collaborate and share analytics-driven reports, visualizations, KPIs, and dashboards with the rest of your team.
SAS® Visual Analytics for SAS® Viya®
By SAS Institute Inc.
SAS® Visual Analytics for SAS® Viya® is a practical guide designed to get you started investigating your data and creating reports with SAS Visual Analytics, a web-based tool that enables you to explore huge volumes of data to identify patterns, trends, and opportunities. Learn how to report, share, and collaborate on insights from data with no SAS programming skills necessary.
Model Risk Management with SAS®
By SAS Institute Inc.
This overview of SAS Model Risk Management prepares you to be an effective and informed participant in the requirements development and solution design phases of your implementation. The book includes hands-on demonstrations, and it teaches key concepts, terminology, and base functionality that is integral to SAS Model Risk Management.
Machine Learning with SAS® Viya®
by SAS Institute Inc.
Machine Learning with SAS® Viya® introduces fundamental concepts of machine learning and the analytics life cycle, including data preparation and extraction. Decision trees, neural networks and support vector machines are covered, followed by information on model assessment and deployment.