R Programming, sas Training – Combo


How long?

  • online
  • on demand



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Full disclaimer.

Who should attend

  • Business Intelligence, Analysts, and Data Scientists
  • Software, SAS developers and IT professionals

What are the prerequisites for taking this Training Course?

There are no specific requirements for taking this Training Course.

About the course

Our R, SAS master program lets you gain proficiency in top statistical computing and data analytics tool. You will work on real work projects in R programming, R-calculator, Operators, Functions, R integration with Hadoop, data mining, statistical analysis, forecasting.

About Course

Master R Programming language and use of SAS Software to apply in Analytical Projects for multiple industrial domains and scenarios

This is a Combo Training Course in the statistical programming language R and business analytics tool SAS. Together these two technologies can help you make sense of data flowing into an organization, decipher, visualize and analyze it on a whole new scale.

What you will learn in this Training Course?

  • Introduction to R programming and SAS tool
  • Learn R-Calculator functionality
  • Work with Stack, Merge and Strsplit
  • Understand matrix manipulation
  • R integration with Hadoop
  • SAS environment and various windows
  • Work with operators, functions and logical statements
  • Study SAS graphs and summary reports
  • Learn about Advanced SAS and Base SAS

Why should you take this Training Course?

Since R is one of the best statistical language and graphical representation techniques and SAS is a highly sophisticated business analytics tool this combo course will give you all the right skills to take up high paying jobs in the business intelligence and analytics domain. You will be able to handle huge amounts of data, create graphical representation, deploy database and spreadsheet data to extract business intelligence out of it.

Course Content

R Programming Course Content

  • Introduction to R

R language for statistical programming, the various features of R, introduction to R Studio, the statistical packages, familiarity with different data types and functions, learning to deploy them in various scenarios, use SQL to apply ‘join’ function, components of R Studio like code editor, visualization and debugging tools, learn about R-bind.

  • R-Packages

R Functions, code compilation and data in well-defined format called R-Packages, learn about R-Package structure, Package metadata and testing, CRAN (Comprehensive R Archive Network), Vector creation and variables values assignment.

  • Sorting Dataframe

R functionality, Rep Function, generating Repeats, Sorting and generating Factor Levels, Transpose and Stack Function.

  • Matrices and Vectors

Introduction to matrix and vector in R, understanding the various functions like Merge, Strsplit, Matrix manipulation, rowSums, rowMeans, colMeans, colSums, sequencing, repetition, indexing and other functions.

  • Reading data from external files

Understanding subscripts in plots in R, how to obtain parts of vectors, using subscripts with arrays, as logical variables, with lists, understanding how to read data from external files.

  • Generating plots

Generate plot in R, Graphs, Bar Plots, Line Plots, Histogram, components of Pie Chart.

  • Analysis of Variance (ANOVA)

Understanding Analysis of Variance (ANOVA) statistical technique, working with Pie Charts, Histograms, deploying ANOVA with R, one way ANOVA, two way ANOVA.

  • K-means Clustering

K-Means Clustering for Cluster & Affinity Analysis, Cluster Algorithm, cohesive subset of items, solving clustering issues, working with large datasets, association rule mining affinity analysis for data mining and analysis and learning co-occurrence relationships.

  • Association Rule Mining

Introduction to Association Rule Mining, the various concepts of Association Rule Mining, various methods to predict relations between variables in large datasets, the algorithm and rules of Association Rule Mining, understanding single cardinality.

  • Regression in R

Understanding what is Simple Linear Regression, the various equations of Line, Slope, Y-Intercept Regression Line, deploying analysis using Regression, the least square criterion, interpreting the results, standard error to estimate and measure of variation.

  • Analyzing Relationship with Regression

Scatter Plots, Two variable Relationship, Simple Linear Regression analysis, Line of best fit

  • Advance Regression

Deep understanding of the measure of variation, the concept of co-efficient of determination, F-Test, the test statistic with an F-distribution, advanced regression in R, prediction linear regression.

  • Logistic Regression

Logistic Regression Mean, Logistic Regression in R.

  • Advance Logistic Regression

Advanced logistic regression, understanding how to do prediction using logistic regression, ensuring the model is accurate, understanding sensitivity and specificity, confusion matrix, what is ROC, a graphical plot illustrating binary classifier system, ROC curve in R for determining sensitivity/specificity trade-offs for a binary classifier.

  • Receiver Operating Characteristic (ROC)

Detailed understanding of ROC, area under ROC Curve, converting the variable, data set partitioning, understanding how to check for multicollinearlity, how two or more variables are highly correlated, building of model, advanced data set partitioning, interpreting of the output, predicting the output, detailed confusion matrix, deploying the Hosmer-Lemeshow test for checking whether the observed event rates match the expected event rates.

  • Kolmogorov Smirnov Chart

Data analysis with R, understanding the WALD test, MC Fadden’s pseudo R-squared, the significance of the area under ROC Curve, Kolmogorov Smirnov Chart which is non-parametric test of one dimensional probability distribution.

  • Database connectivity with R

Connecting to various databases from the R environment, deploying the ODBC tables for reading the data, visualization of the performance of the algorithm using Confusion Matrix.

  • Integrating R with Hadoop

Creating an integrated environment for deploying R on Hadoop platform, working with R Hadoop, RMR package and R Hadoop Integrated Programming Environment, R programming for MapReduce jobs and Hadoop execution.

  • R Case Studies

Logistic Regression Case Study

In this case study you will get a detailed understanding of the advertisement spends of a company that will help to drive more sales. You will deploy logistic regression to forecast the future trends, detect patterns, uncover insights and more all through the power of R programming. Due to this the future advertisement spends can be decided and optimized for higher revenues.

Multiple Regression Case Study

You will understand how to compare the miles per gallon (MPG) of a car based on the various parameters. You will deploy multiple regression and note down the MPG for car make, model, speed, load conditions, etc. It includes the model building, model diagnostic, checking the ROC curve, among other things.

Receiver Operating Characteristic (ROC) case study

You will work with various data sets in R, deploy data exploration methodologies, build scalable models, predict the outcome with highest precision, diagnose the model that you have created with various real world data, check the ROC curve and more.

SAS Course Content

  • Introduction to SAS

Installation and introduction to SAS, how to get started with SAS, understanding the different SAS Windows, how to work with data sets, the various SAS Windows like Output, Search, Editor, Log, Explorer, understanding the SAS Functions, which are the various Library Types and programming files

  • SAS Enterprise Guide

How to import and export raw data files, how to read and subset the data sets, the different statements like SET, MERGE, WHERE

Hands-on Exercise – How to import the Excel file in the Workspace, how to read data and exporting the Workspace to save the data

  • SAS Operators & Functions

The different SAS Operators like Logical, COmparison, Arithmetic, deploying the different SAS Functions like Character, Numeric, Is Null, Contains, Like, Input/Output, along with the conditional statements like If/Else, Do While, Do Until and so on.

Hands-on Exercise – Performing operations using the SAS Functions, logical and arithmetic 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 SAS

Hands-on Exercise – Optimize data, generate rtf, pdf, html and doc files


Macro Syntax, Macro Variables, Positional Parameters in a Macro, Macro Step

Hands-on Exercise – Write a macro, Use positional parameters


SQL Statements in SAS, SELECT, CASE, JOIN, UNION, Sorting Data

Hands-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 PRINT

Hands-on Exercise – Use PROC SORT to sort the results, List ODS, Find mean using PROC Means, print using PROC PRINT

R Programming and SAS Projects

What projects I will be working on this R Programming training?

Project 1

Domain – Restaurant Revenue Prediction

Data set – Sales

Project Description – This project involves predicting the sales of a restaurant on the basis of certain objective measurements. This project will give real time industry experience on handling multiple use cases and derive the solution. This project gives insights about feature engineering and selection.

Project 2

Domain – Data Analytics

Objective – To predict about the class of a flower using its petal’s dimensions

Project 3

Domain – Finance

Objective – 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

Project 4

Domain – Stock Market

Objective – This project focuses on Machine Learning by creating predictive data model to predict future stock prices

What projects I will be working on this SAS training?

Project 1 – Build analytical solution for patients taking medicines

Domain: Health Care

Objective – 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.

Project 2 – Build revenue projections reports

Domain: Sales

Objective – 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

Domain: Finance Market

Objective – 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

Project 4

Domain: Analytics

Objective – k-Means Cluster analysis on Iris dataset to predict about the class of a flower using its petal’s dimensions


David Callaghan

An experienced Blockchain Professional who has been bringing integrated Blockchain, particularly Hyperledger and Ethereum, and Big Data solutions to the cloud, David Callaghan has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marqu...

Suresh Paritala

A Senior Software Architect at NextGen Healthcare who has previously worked with IBM Corporation, Suresh Paritala has worked on Big Data, Data Science, Advanced Analytics, Internet of Things and Azure, along with AI domains like Machine Learning and Deep Learning. He has successfully implemented ...

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R Programming, sas Training – Combo at IntelliPaat

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