Data Science with SAS


Why this course ?

  • This course helps you prepare you for the following certification exam(s): (I) SAS Certified Base Programmer for SAS 9 (II) SAS Certified Advanced Programmer for SAS 9.
  • This course is for SAS programmers who prepare data for analysis.
  • Advanced SAS focuses on the components of the SAS macro facility and how to design, write, and debug macro systems. It also covers how to process SAS data using Structured Query Language (SQL).
  • Ready-to-use procedures handle a wide range of statistical techniques including simple descriptive statistics, data visualization, analysis of variance, regression, categorical data analysis, multivariate analysis, cluster analysis, and non parametric analysis are part of this program

Weekend/Weekday|Live Classes 30% OFF

Program Duration
and Fees

Buy Now


125 Hours




  • Led by an expert instructor
    Instructors are best in the field of Statistics and having industry experience.
    Advantages of attending this course
    No programming knowledge is required for this course.
    Delivered online and classroom
    All coursework is available 24/7 online or offline.



• An Overview of the SAS System
• Introduction to SAS Programs
• Running SAS Programs
• Mastering Fundamental Concepts
• Diagnosing and Correcting Syntax Errors
• Exploring Your SAS Environment
• Getting Started With the PRINT Procedure
• Sequencing and Grouping Observations
• Identifying Observations
• Special WHERE Statement Operators
• Customizing Report Appearance
• Formatting Data Values
• Creating HTML Reports
• Reading Raw Data Files: Column Input
• Reading Raw Data Files: Formatted Input
• Examining Data Errors

• Assigning Variable Attributes
• Changing Variable Attributes
• Reading Excel Spreadsheets
• Reading SAS Data Sets and Creating Variables
• Conditional Processing
• Dropping and Keeping Variables
• Reading Excel Spreadsheets Containing Date Fields
• Match-merging Two or more SAS Data Sets
• Simple Joins Using the SQL Procedure
• Introduction of Summary Reports.
• Basic Summary Reports
• The Report Procedure
• The Tabulate Procedure
• Producing Bar and pie Chart
• Enhancing output
• Producing Plots
• Overview
• Review of SAS basics
• Review of DATA Step Processing
• Review of Displaying SAS Data Sets
• Working with Existing SAS Data Sets
• Outputting Multiple Observations
• Writing to Multiple SAS Data Sets
• Selecting Variables and Observations
• Writing to an External File
• Creating an Accumulating Total variable
• Accumulating Totals for a Group of Data
• Reading Delimited Raw Data Files
• Controlling When a Record Loads
• Reading Hierarchical Raw data Files
• Introduction
• Manipulating Character values
• Manipulating Numeric values
• Manipulating Numeric values based on Dates
• Converting variable Type
• Using the PUT Statement
• Using the DEBUG Option
• Do Loop Processing
• SAS Array Processing
• Using SAS Arrays
• Concatenating SAS Data Sets
• Merging SAS Data Sets
• Combining SAS Data Sets : Additional Features



• overview of SAS Foundation
• course logistics
• course data files
• Introducing the Structured Query Language
• overview of the SQL procedure
• specifying columns
• specifying rows
• presenting data
• summarizing data
• introduction to SQL joins
• inner joins
• outer joins
• complex SQL joins
• noncorrelated subqueries
• in-line views
• Introduction to set operators
• the UNION operator
• the OUTER UNION operator
• the EXCEPT operator
• the INTERSECT operator
• creating tables with the SQL procedure
• creating views with the SQL procedure
• dictionary tables and views
• using SQL procedure options
• interfacing PROC SQL with the macro language
• SAS resources
• Beyond this course
• Introduction to macro variables
• Automatic macro variables
• macro variable references
• user-defined macro variables
• delimiting macro variable references
• macro functions
• defining and calling a macro
• macro parameters
• creating macro variables in the DATA step
• Indirect references to macro variables
• creating macro variables in SQL
• conditional processing
• parameter validation
• iterative processing
• global and local symbol tables



• Types of Analytics
• Properties of Measurements
• Scales of Measurement
• Types of Data
• Measures of Central Tendency
• Measures of Dispersion
• Measures of Location
• Presentation of Data
• Skewness and Kurtosis
• Three Approaches towards Probability
• Concept of a Random Variable
• Probability Mass Function
• Probability Density Function
• Expectation of A Random Variable
• Probability Distributions
• Concept of population and sample
• Techniques of Sampling
• Sampling Distributions
• Concept of estimation
• Different types of Estimation
• Concept of hypothesis
• Null hypothesis
• Alternative hypothesis
• Type-I error
• Type-II error
• Level of Significance
• Confidence Interval
• Parametric Tests and Non Parametric Tests
• One Sample T test
• Two independent sample T test
• Paired Sample T test
• Chi square Test for Independence of Attributes.
• One Way Anova
• Two Way Anova
• Concept of Regression and features of Linear line.
• Assumptions of Classical Linear Model
• Method of Least Squares
• Understanding the Goodness of Fit
• Multiple linear Regression with their Assumptions
• Concept of Multiocollinearity
• Signs of Multicollinearity
• The Idea of Autocorrelation
• Concept and Applications of Logistic Regression
• Principles Behind Logistic Regression
• Comparison between Linear Probability Model and Logistic Regression
• Mathematical Concepts related to Logistic Regression
• Concordant Pairs, Discordant Pairs and Tied Pairs
• Concept of Time Series and its Applications
• Assumptions of Time Series Analysis
• Components of Time Series
• Smoothening techniques
• Stationarity
• Random Walk
• ARIMA Forecasting
• Types of Clusters
• Metric and linkage
• Ward’s Minimum Variance Criteria
• Semi-Partial R-Square and R-Square
• Diagrammatic Representation of clusters
• Problems of Cluster Analysis
• Principal Component Analysis
• Estimating the Initial Communalities
• Eigen Values and Eigen Vectors
• Correlation Matrix check and KMO-MSA check
• Factor loading Matrix
• Diagrammatic Representation of Factors
• Problems of Factor Loadings and Solutions


Download Course

Base SAS





Course Name: Data Science with SAS