Goal: In this module, you will learn to use the SAS Environment and program flow in SAS with specifications of Data and Proc steps. Also, you will learn to install SAS University Edition on your system.

Objective:

At the end of this module, you should be able to:

• Install SAS University Edition 

• Express SAS as a language

• Describe the rules for the definition of SAS names

• Define the different data types, datasets in SAS

• Explain the significance of Data/Procedural step

• Run a simple SAS program

• Exercise better control over Data Step Programming using PDV.

Topics:

• Use Cases of SAS implementation

• Installing SAS University Edition

• Explore SAS GUI, SAS window and its contents

• Rules for the definition of a SAS name

• Different Datatypes in SAS

• Use of Formats and Informatics in SAS

• Illustration of SAS Datasets

• The Data step and procedural step

• Creating an Instream SAS Dataset

• Basic Procedural statements

• Assessing Input Buffer and Program Data Vector (PDV)

Hands-On:

• Navigating the SAS windows environment

• Nuts and bolts of SAS programming steps

• Converting data as per required format

• Creating a dataset using CARDS

• Generating the output using PROC PRINT

Goal: This module introduces you to import datasets and control the import of datasets into SAS and ultimately concatenating them.

Objective: At the end of this module, you should be able to:

• Bring in your external data into SAS and modifying it.

• Describe and subset permanent datasets.

• Modify/Delete existing columns

• Concatenate Datasets

Topics:

• Import of Data in SAS

• Manipulating influx of datasets into SAS

• Creating a new variable

• Working with Temporary and Permanent Datasets

• Set and Merge Statements

Hands-On:

• Demonstrating import of external data within SAS using INFILE

• Demonstrating import of external data within SAS using PROC IMPORT

• Creating Permanent Datasets

• Performing operations with KEEP, DROP and RENAME and LABEL options

• Constructing a new variable

• Creating integrated datasets using SET/MERGE

Goal: This module introduces the concept of looping and its implementation in the SAS programming language. You will explore arrays and functions to simplify complications in SAS programming.

Objective: At the end of this module, you should be able to:

• Demonstrate dependent and independent looping

• Master data step programming with SAS functions

• Simplify processing with SAS Arrays

Topics:

• Conditional& Iterative Processing

• SAS Arrays

• Useful SAS Functions

? PUT/INPUT Functions

? Date/Time Functions

? Numeric Functions

? Character Functions

Hands-On:

• Use of DO, DO WHILE, DO UNTIL, IF-ELSE, ELSE, ELSE IF

• Demonstrate the use of arrays in SAS 

• Operation with INPUT/PUT functions

• Writing a Program with date/time functions

• Performing programming with Numeric functions

• Executing program with Character Functions

Goal: You will learn the analysis of your statistical data with advanced SAS procedures

Objective: At the end of this module, you should be able to:

• Explain Statistical Procedures

• Define priorities to your statistical data

• Discuss statistics associated with a variable

• Express correlation between two or more variables 

Topics:

• Proc Dataset

• Proc Format

• Proc Sort

• Proc Means

• Proc Freq

• Proc Surveyselect

• Proc Transpose

• Proc Summary

• Proc Rank

• Proc Corr

• Proc Univariate

Hands-On:

• Obtaining statistical means of variables

• Checking the degree of dependence within different variables

• Generating ranks for statistical data

• Performing re-structuring of data

• Conducting sampling: Random and Stratified

Goal: You will learn to model estimate and classify events based on the values of dependent variables. You will be taught to perform different types of clustering methodologies to bunch your observations.

Objective: At the end of this module, you should be able to:

• Define clustering and its types

• Explain clustering algorithms

• Nest data in different clusters 

• Analyse the regression between two or multiple variables

• Model and estimate an event based on Logits

Topics:

• Introduction to Clustering

• Hierarchical Clustering

• Non-Hierarchical Clustering (K means Clustering)

• Simple and Multiple Linear Regression

• Logistic Regression

Hands-On:

• Demonstrate the use of PROC CLUSTER 

• Writing a SAS advanced program with PROC FASTCLUS

• Performing operations on regression with PROC REG

• Demonstrate modeling using PROC LOGISTIC