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SAS Training for SAS BASE Certification

Course Summary

SAS Training by Intellipaat will help you to master advance analytics techniques using SAS Language. In this SAS online training course you will learn SAS Macros, Machine Learning, PROC SQL, Procedure, Statistical Analysis and Decision Trees. You will work on real-life projects and prepare for SAS Base programmer certification.

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    Course Syllabus

    SAS Course Content

    Introduction to SAS
    Introduction to Base SAS, Installation of SAS tool, Getting started with SAS, various SAS Windows – Log, Explorer, Output, Search, Editor, etc. working with data sets, overview of SAS Functions, Library Types and programming files
    SAS Enterprise Guide
    Import/Export Raw Data files, reading and sub setting the data set, various statements like WHERE, SET, MergeHands-on Exercise – Import Excel file in workspace, Read data, Export the workspace to save data
    SAS Operators & Functions
    Various SAS Operators – Arithmetic, Logical, Comparison, various SAS Functions – NUMERIC, CHARACTER, IS NULL, CONTAINS, LIKE, Input/Put, Date/Time, Conditional Statements (Do While, Do Until, If, Else)Hands-on Exercise – Apply logical, arithmetic operators and SAS functions to perform operations
    Compilation & Execution
    Understanding about Input Buffer, PDV (Backend), learning what is Missover
    Using Variables
    Defining and Using KEEP and DROP statements, apply these statements, Format and Labels in SAS.Hands-on Exercise – Use KEEP and DROP statements
    Creation and Compilation of SAS Data sets
    Understanding Delimiter, dataline rules, DLM, Delimiter DSD, raw data files and execution, list input for standard data.Hands-on Exercise – Use delimiter rules on raw data files
    SAS Procedures
    The various SAS standard Procedures built-in for popular programs – PROC SORT, PROC FREQ, PROC SUMMARY, PROC RANK, PROC EXPORT, PROC DATASET, PROC TRANSPOSE, , PROC CORR etc.Hands-on Exercise – Use SORT, FREQ, SUMMARY, EXPORT and other procedures
    Input statement and formatted input
    Reading standard and non-standard numeric inputs with Formatted inputs, Column Pointer Controls, Controlling while a record loads, Line pointer control / Absolute line pointer control, Single Trailing , Multiple IN and OUT statements, DATA LINES statement and rules, List Input Method, comparing Single Trailing and Double Trailing.Hands-on Exercise – Read standard and non-standard numeric inputs with Formatted inputs, Control while a record loads, Control a Line pointer, Write Multiple IN and OUT statements
    SAS FORMAT statements – standard and user-written, associating a format with a variable, working with SAS FORMAT, deploying it on PROC Data sets, comparing ATTRIB and FORMAT statements.Hands-on Exercise – Format a variable, deploy format rule on PROC DATA set, Use ATTRIB statement
    SAS Graphs
    Understanding PROC GCHART, various Graphs, Bar Charts – Pie, Bar, 3D, plotting variables with PROC GPLOT.Hands-on Exercise – Plot graphs using PROC GPLOT Display charts using PROC GCHART
    Interactive Data Processing
    SAS advanced data discovery and visualization, point-and-click analytics capabilities, powerful reporting tools.
    Data Transformation Function
    Character Functions, Numeric Functions, Converting Variable Type.Hands-on Exercise – Use Functions in data transformation
    Output Delivery System (ODS)
    Introduction to ODS, Data Optimization, How to generate files (rtf, pdf, html, doc) using SASHands-on Exercise – Optimize data, generate rtf, pdf, html and doc files
    Macro Syntax, Macro Variables, Positional Parameters in a Macro, Macro StepHands-on Exercise – Write a macro, Use positional parameters
    SQL Statements in SAS, SELECT, CASE, JOIN, UNION, Sorting DataHands-on Exercise – Create sql query to select and add a condition
    Use a CASE in select query
    Advanced Base SAS
    Base SAS web-based interface and ready-to-use programs, advanced data manipulation, storage and retrieval, descriptive statistics.Hands-on Exercise – Use web UI to do statistical operations
    Summarization Reports
    Report Enhancement, Global Statements, User-defined Formats, PROC SORT, ODS Destinations, ODS Listing, PROC FREQ, PROC Means, PROC UNIVARIATE, PROC REPORT, PROC PRINTHands-on Exercise – Use PROC SORT to sort the results, List ODS, Find mean using PROC Means, print using PROC PRINT
    SAS Projects
    Project 1 – Categorization of patients based on count of drugs for their therapyDomain: Health CareObjective – This project aims to find out descriptive statistics & subset for specific clinical data problems. It will give them brief insight about BASE SAS procedures and data steps.Problem Statement :Count the number of patients and list of those patients
    1. Who were ever on at least one of the four drugs
    2. Who were ever on each of the four drugs
    3. Who had never been on any drug
    Output should be four datasets
    1. TYPA – Contains the list of patients from problem 1
    2. TYPB – Contains the list of patients from problem 2
    3. TYPC – Contains the list of patients from problem 3
    4. SUMMARY – – Contains the summary of counts for each of 3 problems
    Project 2 – Build revenue projections reportsDomain: SalesObjective – This project will give you hands-on experience in working with the SAS data analytics and business intelligence tool. You will be working on the data entered in a business enterprise setup, aggregate, retrieve and manage that data. You will learn to create insightful reports and graphs and come up with statistical and mathematical analysis to scientifically predict the revenue projection for a particular future time frame. Upon completion of the project you will be well-versed in the practical aspects of data analytics, predictive modeling, and data mining.
    Project 3 :- Impact of pre-paid plans on the preferences of investorsDomain: Finance MarketObjective – The project aims to find the most impacting factors in preferences of pre-paid model, also identifies which are all the variables highly correlated with impacting factors
    Problem Statement :
    • To identify the various reasons for Pre-paid model preference and non-preference among the investors. And also understand the penetration of the Pre-paid model in the brokerage firms
    • To identify the Pre-paid scheme advantages and disadvantages and also identify brand wise market share
    • In addition to this, the project also looks to identify various insights that would help a newly established brand to foray deeper into the market on a large scale
    Project 4 :- k-Means Cluster analysis on Iris datasetDomain: AnalyticsObjective – k-Means Cluster analysis on Iris dataset to predict about the class of a flower using its petal’s dimensions
    Requirements :-
    • Using the famous Iris dataset, predict the class of a flower
    • Perform k-means cluster analysis

Course Fee:
USD 126

Course Type:


Course Status:



1 - 4 hours / week

This course is listed under Development & Implementations and Data & Information Management Community

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