Data Analysis Using SAS

Overview:

Introduction:

SAS (Statistical Analysis System) is a leading software suite widely used for advanced data analysis, statistical modeling, and data visualization. It enables professionals to process large datasets, perform complex analyses, and generate actionable insights. This training program focuses on equipping participants with the skills to effectively utilize SAS for managing, analyzing, and presenting data, ensuring data-driven decision-making and improved organizational outcomes.

Program Objectives:

By the end of this program, participants will be able to:

• Identify the fundamentals of SAS and its applications in data analysis.

• Manage, clean, and transform large datasets using SAS tools.

• Perform statistical analysis and modeling to uncover trends and patterns.

• Create data visualizations and reports for effective communication.

• Utilize SAS for predictive analytics and advanced decision-making.

Targeted Audience:

• Data analysts and statisticians.

• Business intelligence professionals.

• IT and database managers.

• Professionals involved in data-driven decision-making.

Program Outline:

Unit 1:

Introduction to SAS and Data Analysis:

• Overview of SAS and its role in data analytics.

• Key features and components of the SAS environment.

• Basics of SAS programming: syntax and structure.

• Understanding datasets, variables, and data types.

• Importing and exporting data in SAS.

Unit 2:

Data Preparation and Transformation:

• Cleaning and preprocessing raw data for analysis.

• Merging, sorting, and filtering datasets.

• Creating and manipulating variables in SAS.

• Techniques for handling missing and inconsistent data.

Unit 3:

Statistical Analysis and Modeling:

• How to perform descriptive and inferential statistical analysis.

• The process of regression analysis and hypothesis testing in SAS.

• Analysis of variance (ANOVA) and other advanced statistical techniques.

• Methods of building and interpreting predictive models using SAS.

Unit 4:

Data Visualization and Reporting:

• Creating visualizations using SAS procedures: PROC SGPLOT and PROC SGPIE.

• Generating detailed and summary reports in SAS.

• Customizing graphs and charts for stakeholder presentations.

• How to integrate SAS outputs with other reporting tools.

• Effective techniques for communicating analytical findings.

Unit 5:

Advanced SAS Applications and Future Trends:

• Leveraging SAS for predictive analytics and machine learning.

• Automating repetitive tasks and workflows in SAS.

• How to integrate SAS with other platforms and tools.

• Ensuring data security and compliance in SAS environments.

• Exploring future trends and updates in SAS capabilities.