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Data Science, R, Mahout, SAS Training – Combo Course

Course Summary

Our Data Science certification master program lets you become a skilled Data Scientist. We provide the best online training classes to help you learn the various aspects of Data Science like data acquisition, analysis, Apache Mahout, statistical methods, SAS programming, clustering, vectors. Work on critical real world projects.

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    Course Syllabus

    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 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.

    Mahout Course Content

    Mahout Overview

    Classification and Recommendation, Clustering in Mahout, Pattern Mining, Understanding machine Learning, Using Model diagram to decide the approach, Data flow, Supervised and Unsupervised learning

    Mahout Recommendations

    Concept of Recommendation, Recommendations by E-commerce site, Comparison between User Recommendations and Item recommendation, Define recommenders and Classifiers, Process of Collaborative Filtering, Explaining Pearson coefficient algorithm, Euclidean distance measure, Implementing a recommender using map reduce

    Clustering Session 1

    Defining Clustering, User-to-user similarity, Clustering Illustration, Euclidean distance measure, Distance measure vector, Understanding the process of Clustering, Vectorizing documents-Unstructured data

    Clustering Session 2

    Document clustering, Sequence-to-sparse Utility, K-Mean Clustering

    Classification Session 1

    Terminology, Predictor and Target variable, Classifiable DataKey Challenges in Classification algorithm, Vectorizing Continuous data, Classification Examples, Logic Regression and its examples

    Clustering and Classification Session 2

    Clustering, Clustering Process, Transaction Clustering, Different techniques of Vectorization, Distance measure, Clustering algorithm-K-MEAN, Clustering Application-1, Clustering Application-2, Sentiment Analyzer

    Pattern Mining

    Pearson Coefficient, Collaborative Filtering Process, Collaborative Filtering, Similarity Algorithms, Pearson Correlation, Euclidean Distance Measure -Frequent Pattern & Association rules, Frequent Pattern Growth

    Data Science Course Content

    Introduction to Data Science and Statistical Analytics
    Introduction to Data Science, Use cases, Need of Business Analytics, Data Science Life Cycle, Different tools available for Data Science
    Introduction to R
    Installing R and R-Studio, R packages, R Operators, if statements and loops (for, while, repeat, break, next), switch case
    Data Exploration, Data Wrangling and R Data Structure
    Importing and Exporting data from external source, Data exploratory analysis, R Data Structure (Vector, Scalar, Matrices, Array, Data frame, List), Functions, Apply Functions
    Data Visualization
    Bar Graph (Simple, Grouped, Stacked), Histogram, Pi Chart, Line Chart, Box (Whisker) Plot, Scatter Plot, Correlogram
    Introduction to Statistics
    Terminologies of Statistics ,Measures of Centers, Measures of Spread, Probability, Normal Distribution, Binary Distribution, Hypothesis Testing, Chi Square Test, ANOVA
    Predictive Modeling – 1 ( Linear Regression)
    Supervised Learning – Linear Regression ,Bivariate Regression, Multiple Regression Analysis, Correlation( Positive, negative and neutral), Industrial Case Study, Machine Learning Use-Cases, Machine Learning Process Flow, Machine Learning Categories
    Predictive Modeling – 2 ( Logistic Regression)
    Logistic Regression
    Decision Trees
    What is Classification and its use cases?, What is Decision Tree?, Algorithm for Decision Tree Induction, Creating a Perfect Decision Tree, Confusion Matrix
    Random Forest
    Random Forest, What is Naive Bayes?
    Unsupervised learning
    What is Clustering & its Use Cases?, What is K-means Clustering?, What is Canopy Clustering?, What is Hierarchical Clustering?
    Association Analysis and Recommendation engine
    Market Basket Analysis (MBA), Association Rules, Apriori Algorithm for MBA, Introduction of Recommendation Engine, Types of Recommendation – User-Based and Item-Based, Recommendation Use-case
    Sentiment Analysis
    Introduction to Text Mining, Introduction to Sentiment, Setting up API bridge, between R and Tweeter Account, Extracting Tweet from Tweeter Acc, Scoring the tweet
    Time Series
    What is Time Series data?, Time Series variables, Different components of Time Series data, Visualize the data to identify Time Series Components, Implement ARIMA model for forecasting, Exponential smoothing models, Identifying different time series scenario based on which different Exponential Smoothing model can be applied, Implement respective ETS model for forecasting

    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, Merge

    Hands-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 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 Projects

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

    Project 3

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

    Data Science Project

    Project 1 – Understanding Cold Start Problem in Data Science

    Topics: This project involves understanding of the cold start problem associated with the recommender systems. You will gain hands-on experience in information filtering, working on systems with zero historical data to refer to, as in the case of launching a new product. You will gain proficiency in working with personalized applications like movies, books, songs, news and such other recommendations. This project includes the following:

    • Algorithms for Recommender
    • Ways of Recommendation
    • Types of Recommendation -Collaborative Filtering Based Recommendation, Content-Based Recommendation
    • Complete mastery in working with the Cold Start Problem.

    Project 2 – Recommendation for Movie, Summary

    Topics: This is real world project that gives you hands-on experience in working with a movie recommender system. Depending on what movies are liked by a particular user, you will be in a position to provider data-driven recommendations. This project involves understanding recommender systems, information filtering, predicting ‘rating’, learning about user ‘preference’ and so on. You will exclusively work on data related to user details, movie details and others. The main components of the project include the following:

    • Recommendation for movie
    • Two Types of Predictions – Rating Prediction, Item Prediction
    • Important Approaches: Memory Based and Model-Based
    • Knowing User Based Methods in K-Nearest Neighbor
    • Understanding Item Based Method
    • Matrix Factorization
    • Decomposition of Singular Value
    • Data Science Project discussion
    • Collaboration Filtering
    • Business Variables Overview
    Statistics and Probability Project

    Project – Data Analysis Project

    Data – Sales

    Problem Statement – It includes the following actions:

    Understand the business solutions, Discussion with the warehouse team, Data Collection & Storage, Data Cleaning, Build a Hypothesis Tree around the business problem, Produce the final result.

    SAS Projects

    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

Course Fee:
USD 517

Course Type:


Course Status:



1 - 4 hours / week

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Winner of
Top 100 Asia
Finalist at SiTF Awards 2014 under the category Best Social & Community Product
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Finalist at HR Vendor of the Year 2015 Awards under the category Best Talent Management Software
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