Goal: This session will teach you how to use the SAS Environment and programme flow in SAS using Data and Proc step requirements. You will also learn how to install SAS University Edition on your machine.

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 definition of a SAS name

• Different Datatypes in SAS

• Use of Formats and Informats 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 lesson teaches you how to import datasets and regulate their import into SAS before concatenating them.

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

• Bring in your external data into SAS and modify 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 topic explains looping and how it is implemented in the SAS programming language. You will investigate arrays and functions in order to ease complexities 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 categorize occurrences based on dependent variable values. You will be taught how to use several clustering approaches to group 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 

• Analyze 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

Goal: You will learn how to decrease the complexity of a problem by constructing and resolving optimization models and eventually routing comprehensive presentation output from SAS in desired formats and destinations.

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

• Discuss data optimization

• Acquire optimized models for complex problems

• Resolve optimization models

• Describe the Output Delivery System in SAS

• Explain the use of ODS statement for creation of different file types

Topics:

• What is Data Optimization?

• Realizing Optimization Models

• Using Proc Optmodel

• Solving the Rosenbrock Problem

• Introduction to ODS

• Benefits of using ODS

• Generating rtf file

• Generating pdf file

• Generating html file

• Generating doc file

Hands On:

• Writing a program with PROC OPTMODEL

• Solving the optimization model using SOLVE

• Extracting optimized outputs 

• Routing quality presentation files

• Create html files

• Create rtf file

• Create pdf and doc files