Course Outline:

SAS Programming 1: Essentials

• Navigate the SAS windowing environment
• Navigate the SAS Enterprise Guide programming environment
• Read various types of data into SAS data sets
• Create SAS variables and subset data
• Combine SAS data sets
• Create and enhance listing and summary reports
• Validate SAS data sets.

SAS Programming 2: Data Manipulation Techniques

• Control SAS data set input and output
• Combine SAS data sets
• Summarize, read, and write different types of data
• Perform DO loop and SAS array processing
• Transform character, numeric, and date variables.

SAS Macro Language 1: Essentials

• Perform text substitution in SAS code
• Automate and customize the production of SAS code
• Conditionally or iteratively construct SAS code
• Use macro variables and macro functions.

SAS SQL 1: Essentials

• Query and subset data
• Summarize and present data
• Combine tables, including complex joins and merges
• Create and modify table views and indexes
• Replace multiple DATA and PROC steps with one SQL query.

SAS Programming 3: Advanced Techniques and Efficiencies

• Benchmark computer resource usage
• Control memory, I/O, and CPU resources
• Create and use indexes
• Combine data horizontally
• Use hash and hiter DATA step component objects and arrays as lookup tables
• Compress SAS data sets
• Sample your SAS data sets
• Create and use SAS data views
• Safely reduce the length of numeric variables
• Create user-defined functions and informats.

SAS Enterprise Guide: ANOVA, Regression, and Logistic Regression

• Generate descriptive statistics and explore data with graphs
• Perform analysis of variance
• Perform linear regression and assess the assumptions
• Use diagnostic statistics to identify potential outliers in multiple regression
• Use chi-square statistics to detect associations among categorical variables
• Fit a multiple logistic regression model.

Applied Analytics Using SAS Enterprise Miner

• Define a SAS Enterprise Miner project and explore data graphically
• Modify data for better analysis results
• Build and understand predictive models such as decision trees and regression models
• Compare and explain complex models
• Generate and use score code

The fees is inclusive of:

• Fee: ₹2,41,000/-   ₹1,36,000+GST | Duration: 144 Hours.
• Training & Digital Badge.
• Course material.
• 2 attempts of Global certification.