SAS Programming

• Experience with navigating folder structures

Audience

• Data Analysts

SAS is a statistical software platform for predictive analysis, data management, advanced analytics, and more. With SAS programming, users are able to automate repetitive processes, manage big data sets, etc.

This instructor-led, live training (online or onsite) is aimed at data analysts who wish to program in SAS for advanced data analysis.

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

• Master macros to write efficient SAS programs.
• Train a model and make predictions on unseen data with predictive modeling.
• Create charts and plots for data visualization.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction

SAS in Depth

• SAS data sets
• SAS variables
• SAS libraries
• SAS code structure

Preparing the Development Environment

• Installing and configuring SAS Studio
• Installing and configuring WPS

Data management in SAS

• Importing data
• Exporting data
• Creating variables and calculations
• Filtering observations
• Creating conditionals and loops
• Merging data sets
• Using statements
• Cleaning data

Arrays and Functions

• Recording new variables with loops
• Constructing new variables
• Using built in SAS functions
• Combining raw data files

Data Visualization

• Creating a bar chart
• Creating a scatter plot
• Creating a pie graph
• Overlaying plots

Statistics Analysis

• Reporting data
• Using linear regressions
• Using multiple regressions
• Interpreting data
• Making predictions

SAS SQL

• SAS SQL syntax
• Using clauses and statements
• Working with columns and rows
• Working with tables

SAS Index

• Testing with data sets
• Using PROC
• Creating, updating, and applying an index

SAS Macro

• Using macro variables
• Using macro functions
• Creating a Macro
• Debugging and storing Macros

Predictive Modeling

• Using linear regressions
• Using multiple regressions
• Evaluating data patterns
• Using input variables
• Working with PROC MI

Summary and Conclusion