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. Learn how to

• ingest and blend data from a variety of data sources
• visually analyze and profile data for better understanding of quality issues
• collaborate and share data on data management projects
• wrangle data in a self-service data preparation environment with no coding skills
• leverage custom SAS code preparing analytic-driven data
• examine relationships between data to assess impact and aid in governance
• create and schedule repeatable workflows to automate time-consuming data preparation tasks.

Who should attend Business and data analysts, data miners, data scientists, citizen data scientists, statisticians, and others who interact with data from a variety of sources to perform data discovery, wrangling, blending and manipulation to ensure the data is fit-for-purpose in analytical models, explorations, and reports. Formats available Duration
: 4 half-day session(s)

No SAS experience or programming experience is required, although you should have some computer experience. Specifically, you should be able to

• log on and off a computer and use a keyboard or mouse
• use a web browser to access information.

This course addresses SAS Viya software.

Introduction to SAS Viya and SAS Data Preparation

• overview of the SAS Viya platform and its capabilities
• understand the critical capabilities of analytic data preparation
• understand the capabilities of SAS Data Preparation to create analytic-driven data

Collaborate on Data Management Projects

• understand the fundamental requirements of a data socialization platform
• create a data management project to promote data reuse, consistency, and team collaboration

Discover, Ingest, and Explore Data

• understand best practices and effective data ingestion considerations for exploring data
• ingest and filter data from a variety of sources, including SAS data sets and social media
• profile data to better understand the quality and structure

Self-Service Data Preparation for Analytics-Ready Data

• create and manage target tables and their columns
• cleanse data using data quality transforms that leverage the SAS Quality Knowledge Base (QKB)
• create data preparation plan files to track, view, and modify changes made to data

Transform Data into Analytics-Ready Data

• manage and aggregate target table columns
• leverage SAS functions and CAS action sets
• cleanse data using data quality transforms and the SAS Quality Knowledge Base
• filter and transpose table data rows
• create data preparation plan files to track and schedule data preparation tasks

DIDMPV