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.

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 when 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 that the data is fit for purpose in analytical models, explorations, and reports

Formats available	Standard duration
Classroom:	2.0 days
Live Web Classroom:	4 half-day session(s)
e-Learning:	14 hours/180 day license

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

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

Data Governance and Lineage

• explore relationships among data sources, data objects, and actions taken on data
• analyze data object dependencies to determine impact analysis
• trace data curation pipeline activity