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SAS® Data Analyst

Course number: CGISAS40

The SAS Training and Certification Course is designed to provide concept and skills of SAS Programming language, tools and several leading statistical techniques to become a successful Analytics professional. These techniques can be enforced to carry out advanced SAS programming. The course includes a series of interactive exercises, which allows for more effective learning.

  • Associate or Bachelor's degree with at least 2 years of work experience
Target Audience

There is an increasing demand for skilled data scientists across all industries that makes this course suitable for participants at all levels of experience. We recommend this data science training especially for the following professionals:

  • Analytics professionals who want to work with SAS
  • IT professionals looking for a career switch in the fields of analytics
  • Software developers interested in pursuing a career in analytics
  • Graduates looking to build a career in analytics and data science
  • Experienced professionals who would like to harness data science in their fields

SAS Certified Data Scientist


This exam is administered by SAS and Pearson VUE.
50-55 multiple-choice and short-answer questions.
(Must achieve score of 70 percent correct to pass)
100 minutes to complete exam.
Use exam ID A00-211; required when registering with Pearson VUE.
This exam is based on SAS 9.4.


Post class completion, students can appear for the exam which is based on SAS 9.4.

Course Outline
Module 1: Getting Started With SAS

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.

Objectives – 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 datatypes, datasets in SAS
  • Explain the significance of Data/Procedural step
  • Run a simple SAS program
  • Exercise better control over Data Step Programming using PDV


  • 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)


  • 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
Module 2: Processing and Integrating with the Data Step

Goal –    This module introduces you to importing datasets and controlling the import of datasets into SAS, and ultimately concatenating them.

Objectives – 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


  • 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


  • 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
Module 3: Customizing Datasets

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.

Objectives – 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


  • Conditional& Iterative Processing
  • SAS Arrays
  • Useful SAS Functions
    • PUT/INPUT Functions
    • Date/Time Functions
    • Numeric Functions
    • Character Functions


  • 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 Function
Module 4: Advanced SAS Procedures

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

Objectives – 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


  • Proc Dataset
  • Proc Format
  • Proc Sort
  • Proc Means
  • Proc Freq
  • Proc Surveyselect
  • Proc Transpose
  • Proc Summary
  • Proc Rank
  • Proc Corr
  • Proc Univariate


  • 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
Module 5: Advanced Statistical Proficiency with SAS

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.

Objectives – 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


  • Introduction to Clustering
  • Hierarchical Clustering
  • Non-Hierarchical Clustering (K means Clustering)
  • Simple and Multiple Linear Regression
  • Logistic Regression


  • Demonstrate the use of PROC CLUSTER
  • Writing a SAS advanced program with PROC FASTCLUS
  • Performing operations on regression with PROC REG
  • Demonstrate modelling using PROC LOGISTIC
Module 6: Optimization of variables and SAS ODS

Goal – You will learn to reduce the degree of complexity of a problem by developing& resolving optimization models and ultimately routing detailed presentation output from SAS in desired formats and destination.

Objectives – 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


  • 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


  • 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
Module 7: PROC SQL

Goal – You will learn to invoke SQL in SAS.

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

  • Write SQL code using various styles of the SELECT statement
  • Use CASE/WHEN clauses for conditionally processing the data
  • Systemize appearance of observations
  • Join data from two or more data sets


  • Creating new tables
  • The SELECT statement
  • Sorting Data
  • The CASE expression
  • Other SELECT statement clauses


  • Demonstrating the SQL Procedure
  • Writing the SELECT clause
  • Application of WHERE clause
  • Merging Datasets
  • Using ORDER BY clause
Module 8: Using SAS Macros

Goal – You will explore how to automate a complex repeated process in SAS with the use of Macros.

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

  • Define Macro Concept
  • Automate a task that you perform repeatedly
  • Explain Macro Step
  • Express Macro step as a combination of different variables


  • Introduction to Macros
  • Benefits of using SAS Macros
  • Macro Code Constituents
  • Macro Variables
  • Macro Step
  • Positional Parameters to Macros


  • Demonstrate the use of Macro variables in SAS
  • Writing a Macro code to simplify a program
  • Demonstrate Macro step programming
  • Performing Macro coding by passing parameters
Certification Project

Towards end of the course, you will get an opportunity to work on a live case study from the banking domain which happens to be one of the most data intensive industries.

Industry: Banking

Data: Customer-level, Accounts-level and Transaction level

Problem-statement: To build a Customer Analytics Record.

A unique feature of this project is that though it will be mimicked for a bank, it has much wider application across almost all the B2C (and even a few B2B) industries.

Available Formats

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