Overview
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.
Requirements
- Experience with navigating folder structures
Audience
- Data Analysts
Course Outline
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