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Home / Data Science Programs / **Data Science, R, Mahout, SAS Training – Combo Course**

Preview this Course

- Introduction to the role of Data Scientist
- Learn programming in R language
- Mahout Machine Learning Algorithms
- Learn SAS Programming
- Learn about Project Life Cycle in Data Science
- Understand Vector Creation and Variable Value Assignment
- Learn about Database Connectivity
- Get to know Data collection, conversion & interpretation
- Understand Linear and Logistic Regression
- Learn Clustering and Vectorizing data
- The concepts of Statistics
- Probability rules and Bayes Theorem
- Get to know Sampling methods and Plotting techniques
- Learn the concepts of Tables and Data Analysis
- Integrate R with Hadoop

- Data Scientists, Analysts, Machine Learning professionals, Statistician
- Programmers, Business Intelligence professionals, Information Architects, Project Managers
- Those looking to work in Data Science

There are no prerequisites for taking this Training Course.

This is a complete Training Course in the field of Data Science and Data Analysis that can make you industry-ready. You will gain deep expertise in multiple technologies and platforms including project management. This Course will equip you with the much-needed programming expertise in R, learn about Apache Mahout, and get to know the techniques of Statistics and Probability. All in all you will have the right skills to work in the Data Science field in the best companies around the world at top salaries.

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.

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

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.

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.

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

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 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.

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.

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.

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

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 Mean, Logistic Regression in R.

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.

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.

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.

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.

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.

**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.

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

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

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

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

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

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

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

What is Data Science, significance of Data Science in today’s digitally-driven world, applications of Data Science, lifecycle of Data Science, components of the Data Science lifecycle, introduction to big data and Hadoop, introduction to Machine Learning and Deep Learning, introduction to R programming and R Studio.

**Hands-on Exercise – **Installation of R Studio, implementing simple mathematical operations and logic using R operators, loops, if statements and switch cases.

Introduction to data exploration, importing and exporting data to/from external sources, what is data exploratory analysis, data importing, dataframes, working with dataframes, accessing individual elements, vectors and factors, operators, in-built functions, conditional, looping statements and user-defined functions, matrix, list and array.

**Hands-on Exercise – **Accessing individual elements of customer churn data, modifying and extracting the results from the dataset using user-defined functions in R.

Need for Data Manipulation, Introduction to dplyr package, Selecting one or more columns with select() function, Filtering out records on the basis of a condition with filter() function, Adding new columns with the mutate() function, Sampling & Counting with sample_n(), sample_frac() & count() functions, Getting summarized results with the summarise() function, Combining different functions with the pipe operator, Implementing sql like operations with sqldf, Text Mining with StringR, wordcloud & StringR, Data Manipulation with data.table package, Working with dates with the lubridate package.

**Hands-on Exercise – **Implementing dplyr to perform various operations for abstracting over how data is manipulated and stored.

Introduction to visualization, Different types of graphs, Introduction to grammar of graphics & ggplot2 package, Understanding categorical distribution with geom_bar() function, understanding numerical distribution with geom_hist() function, building frequency polygons with geom_freqpoly(), making a scatter-plot with geom_pont() function, multivariate analysis with geom_boxplot, univariate Analysis with Bar-plot, histogram and Density Plot, multivariate distribution, Bar-plots for categorical variables using geom_bar(), adding themes with the theme() layer, visualization with plotly package & ggvis package, geographic visualization with ggmap(), building web applications with shinyR, frequency-plots with geom_freqpoly(), multivariate distribution with scatter-plots and smooth lines, continuous vs categorical with box-plots, subgrouping the plots, working with co-ordinates and themes to make the graphs more presentable, Intro to plotly & various plots, visualization with ggvis package, geographic visualization with ggmap(), building web applications with shinyR.

**Hands-on Exercise – **Creating data visualization to understand the customer churn ratio using charts using ggplot2, Plotly for importing and analyzing data into grids. You will visualize tenure, monthly charges, total charges and other individual columns by using the scatter plot.

Why do we need Statistics?, Categories of Statistics, Statistical Terminologies,Types of Data, Measures of Central Tendency, Measures of Spread, Correlation & Covariance,Standardization & Normalization,Probability & Types of Probability, Hypothesis Testing, Chi-Square testing, ANOVA, normal distribution, binary distribution.

**Hands-on Exercise – **Building a statistical analysis model that uses quantifications, representations, experimental data for gathering, reviewing, analyzing and drawing conclusions from data.

Introduction to Machine Learning, introduction to Linear Regression, predictive modeling with Linear Regression, simple Linear and multiple Linear Regression, concepts and formulas, assumptions and residual diagnostics in Linear Regression, building simple linear model, predicting results and finding p-value, introduction to logistic regression, comparing linear regression and logistics regression, bivariate & multi-variate logistic regression, confusion matrix & accuracy of model, threshold evaluation with ROCR, uses of Poisson Regression, bivariate & multivariate Poisson Regression, implementing Poisson Regression in R, Linear Regression concepts and detailed formulas, various assumptions of Linear Regression,residuals, qqnorm(), qqline(), understanding the fit of the model, building simple linear model, predicting results and finding p-value, understanding the summary results with Null Hypothesis, p-value & F-statistic, building linear models with multiple independent variables.

**Hands-on Exercise – **Modeling the relationship within the data using linear predictor functions. Implementing Linear & Logistics Regression in R by building model with ‘tenure’ as dependent variable and multiple independent variables.

Introduction to Logistic Regression, Logistic Regression Concepts, Linear vs Logistic regression, math behind Logistic Regression, detailed formulas, logit function and odds, Bi-variate logistic Regression, Poisson Regression, building simple “binomial” model and predicting result, confusion matrix and Accuracy, true positive rate, false positive rate, and confusion matrix for evaluating built model, threshold evaluation with ROCR, finding the right threshold by building the ROC plot, cross validation & multivariate logistic regression, building logistic models with multiple independent variables, real-life applications of Logistic Regression.

**Hands-on Exercise – **Implementing predictive analytics by describing the data and explaining the relationship between one dependent binary variable and one or more binary variables. You will use glm() to build a model and use ‘Churn’ as the dependent variable.

What is classification and different classification techniques, introduction to Decision Tree, algorithm for decision tree induction, building a decision tree in R, creating a perfect Decision Tree, Confusion Matrix, Regression trees vs Classification trees, introduction to ensemble of trees and bagging, Random Forest concept, implementing Random Forest in R, what is Naive Bayes, Computing Probabilities, Laplace Correction, Implementing Naive Bayes in R, What is KNN algorithm, implementing KNN in R, what is Support Vector Machine, implementing SVM in R, what is XGBOOST, Implementing XGBOOST in R, Impurity Function – Entropy, understand the concept of information gain for right split of node, Impurity Function – Information gain, understand the concept of Gini index for right split of node, Impurity Function – Gini index, understand the concept of Entropy for right split of node, overfitting & pruning, pre-pruning, post-pruning, cost-complexity pruning, pruning decision tree and predicting values, find the right no of trees and evaluate performance metrics.

**Hands-on Exercise – **Implementing Random Forest for both regression and classification problems. You will build a tree, prune it by using ‘churn’ as the dependent variable and build a Random Forest with the right number of trees, using ROCR for performance metrics.

What is Clustering & it’s Use Cases, what is K-means Clustering, what is Canopy Clustering, what is Hierarchical Clustering, introduction to Unsupervised Learning, feature extraction & clustering algorithms, k-means clustering algorithm, Theoretical aspects of k-means, and k-means process flow, K-means in R, implementing K-means on the data-set and finding the right no. of clusters using Scree-plot, hierarchical clustering & Dendogram, understand Hierarchical clustering, implement it in R and have a look at Dendograms, Principal Component Analysis, explanation of Principal Component Analysis in detail, PCA in R, implementing PCA in R.

**Hands-on Exercise – **Deploying unsupervised learning with R to achieve clustering and dimensionality reduction, K-means clustering for visualizing and interpreting results for the customer churn data.

Introduction to association rule Mining & Market Basket Analysis, measures of Association Rule Mining: Support, Confidence, Lift, Apriori algorithm & implementing it in R, Introduction to Recommendation Engine, user-based collaborative filtering & Item-Based Collaborative Filtering, implementing Recommendation Engine in R, user-Based and item-Based, Recommendation Use-cases.

**Hands-on Exercise – **Deploying association analysis as a rule-based machine learning method, identifying strong rules discovered in databases with measures based on interesting discoveries.

What is Time Series, techniques and applications, components of Time Series, moving average, smoothing techniques, exponential smoothing, univariate time series models, multivariate time series analysis, Arima model, Time Series in R, sentiment analysis in R (Twitter sentiment analysis), text analysis.

**Hands-on Exercise – **Analyzing time series data, sequence of measurements that follow a non-random order to identify the nature of phenomenon and to forecast the future values in the series.

Introducing Artificial Intelligence and Deep Learning, what is an Artificial Neural Network, TensorFlow – computational framework for building AI models, fundamentals of building ANN using TensorFlow, working with TensorFlow in R.

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

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

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.

Understanding about Input Buffer, PDV (Backend), learning what is Missover

Defining and Using KEEP and DROP statements, apply these statements, Format and Labels in SAS.

**Hands-on Exercise – **Use KEEP and DROP statements

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

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

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

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

SAS advanced data discovery and visualization, point-and-click analytics capabilities, powerful reporting tools.

Character Functions, Numeric Functions, Converting Variable Type.**Hands-on Exercise – **Use Functions in data transformation

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

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

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

**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

**The Market Basket Analysis (MBA) case study**

This case study is associated with the modeling technique of Market Basket Analysis where you will learn about loading of data, various techniques for plotting the items and running the algorithms. It includes finding out what are the items that go hand in hand and hence can be clubbed together. This is used for various real world scenarios like a supermarket shopping cart and so on.

**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.

**Project 1 : **Augmenting retail sales with Data Science

**Industry : **Retail

**Problem Statement : **How to deploy the various rules and algorithms of Data Science for analyzing stationary store purchase data**.**

**Topics : **In this project you will deploy the various tools of Data Science like association rule, Apriori algorithm in R, support, lift and confidence of association rule. You will analyze the purchase data of the stationary outlet for three days and understand the customer buying patterns across products.

**Highlights:**

- Association rules for transaction data
- Association mining with Apriori algorithm
- Generating rules and identifying patterns.

**Project 2 : Analyzing pre-paid model of stock broking**

**Industry : **Finance

**Problem Statement :** Finding out the deciding factor for people to opt for the pre-paid model of stock broking.

**Topics : **In this Data Science project you will learn about the various variables that are highly correlated in pre-paid brokerage model, analysis of various market opportunities, developing targeted promotion plans for various products sold under various categories. You will also do competitor analysis, the advantages and disadvantages of pre-paid model.

**Highlights :**

- Deploying the rules of statistical analysis
- Implementing data visualization
- Linear regression for predictive modeling.

**Project 3 : **Cold Start Problem in Data Science

**Industry :** Ecommerce

**Problem Statement :** how to build a recommender system without the historical data available

**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 various ways of working with algorithms and deploying other data science techniques.

**Highlight :**

- 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 4 :** 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 provide 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

**Project 5 : **Prediction on Pokemon dataset

**Industry :**Gaming

**Problem Statement :**For the purpose of this case study, you are a Pokemon trainer who is on his way to catch all the 800 Pokemons

**Topics :**This real-world project will give you a hands-on experience on the data science life cycle. You’ll understand the structure of the ‘Pokemon’ dataset & use machine learning algorithms to make some predictions. You will use the dplyr package to filter out specific Pokemons and use decision trees to find if the Pokemon is legendary or not.

**Highlight :**

- dplyr package to filter Pokemons
- Decision Tree algorithm
- Linear regression algorithm.

**Project 6 : **Book Recommender System

**Industry :**E-commerce

**Problem Statement :**Building a book recommender system for readers with similar interests

**Topics :**This real-world project will give you a hands-on experience in working with a book recommender system. Depending on what books are read by a particular user, you will be in a position to provide data-driven recommendations. You will understand the structure of the data and visualize it to find interesting patterns.

**Highlight :**

- Data analysis & visualization
- Recommender Lab
- User Based Collaborative Filtering Model.

**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.

**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

Intellipaat is leader in providing Data Science training. Become proficient in implementing sophisticated business and data analytics models using concepts of Data science, R Programming, Apache Mahout and Statistics and Probability. This training course is fully aligned towards clearing the **CCP Data Scientist Cloudera certification (CCP:DS)**.

You will be working on real time projects that have high relevance in the corporate world, step by step assignments and curriculum designed by industry experts. Upon completion of the training course you can apply for some of the best jobs in top MNCs around the world at top salaries. Intellipaat offers lifetime access to videos, course materials, 24/7 Support, and course material upgrading to latest version at no extra fees. **Hence it is clearly a one-time investment**.

At Intellipaat you can enroll either for the instructor-led online training or self-paced training. Apart from this Intellipaat also offers corporate training for organizations to upskill their workforce. All trainers at Intellipaat have 12+ years of relevant industry experience and they have been actively working as consultants in the same domain making them subject matter experts. Go through the sample videos to check the quality of the trainers.

Intellipaat is offering the 24/7 query resolution and you can raise a ticket with the dedicated support team anytime. You can avail the email support for all your queries. In the event of your query not getting resolved through email we can also arrange one-to-one sessions with the trainers. You would be glad to know that you can contact Intellipaat support even after completion of the training. We also do not put a limit on the number of tickets you can raise when it comes to query resolution and doubt clearance.

Intellipaat offers the self-paced training to those who want to learn at their own pace. This training also affords you the benefit of query resolution through email, one-on-one sessions with trainers, round the clock support and access to the learning modules or LMS for lifetime. Also you get the latest version of the course material at no added cost. The Intellipaat self-paced training is 75% lesser priced compared to the online instructor-led training. If you face any problems while learning we can always arrange a virtual live class with the trainers as well.

Intellipaat is offering you the most updated, relevant and high value real-world projects as part of the training program. This way you can implement the learning that you have acquired in a real-world industry setup. All training comes with multiple projects that thoroughly test your skills, learning and practical knowledge thus making you completely industry-ready. You will work on highly exciting projects in the domains of high technology, ecommerce, marketing, sales, networking, banking, insurance, etc. Upon successful completion of the projects your skills will be considered equal to six months of rigorous industry experience.

Intellipaat actively provides placement assistance to all learners who have successfully completed the training. For this we are exclusively tied-up with over 80 top MNCs from around the world. This way you can be placed in outstanding organizations like Sony, Ericsson, TCS, Mu Sigma, Standard Chartered, Cognizant, Cisco, among other equally great enterprises. We also help you with the job interview and résumé preparation part as well.

You can definitely make the switch from self-paced to online instructor-led training by simply paying the extra amount and joining the next batch of the training which shall be notified to you specifically.

Once you complete the Intellipaat training program along with all the real-world projects, quizzes and assignments and upon scoring at least 60% marks in the qualifying exam; you will be awarded the Intellipaat verified certification. This certificate is very well recognized in Intellipaat affiliate organizations which include over 80 top MNCs from around the world which are also part of the Fortune 500 list of companies.

This course is designed for clearing the **SAS Certified Base Programmer certification exam**. The entire training course content is in line with respective certification program and helps you clear the requisite certification exam with ease and get the best jobs in the top MNCs.

As part of this training you will be working on real time projects and assignments that have immense implications in the real world industry scenario thus helping you fast track your career effortlessly.

At the end of this training program there will be quizzes that perfectly reflect the type of questions asked in the respective certification exams and helps you score better marks in certification exam.

**Intellipaat R, Mahout, Data Science and the Intellipaat Course Completion certificate** will be awarded on the completion of Project work (upon expert review) and on scoring of at least 60% marks in the quiz. Intellipaat certification is well recognized in top 80+ MNCs like Ericsson, Cisco, Cognizant, Sony, Mu Sigma, Saint-Gobain, Standard Chartered, TCS, Genpact, Hexaware, etc.

Suresh Paritala

A senior software architect at NextGen Healthcare who has previously worked with IBM Corporation. Suresh has worked on Big Data, Data Science, advanced analytics, Internet of Things, Azure along with AI domains like Machine Learning and Deep Learning. He has successfully implemented high impact projects in major corporations around the world.

Samanth Reddy

A renowned Data Scientist who has worked with Google and currently working at ASCAP. Samanth has a proven ability to develop Data Science strategies that have a high impact on the revenues of organizations. He comes with strong Data Science expertise and has created decisive Data Science strategies for Fortune 500 Corporations.

David Callaghan

**An experienced blockchain professional** who has been bringing integrated blockchain particularly Hyperledger and Ethereum and big data solutions to the cloud. David has previously worked on Hadoop, AWS Cloud, Big Data and Pentaho projects that have had major impact on revenues of marquee brands around the world.

Self Paced

Lifetime Access and 24/7 Support

Key Features

Self-paced Videos : 65 Hrs

High quality interactive e-learning sessions for Self paced course.
For online instructor led training, total course will be divided into sessions.

Exercises & Project Work : 106 Hrs

Each module will be followed by practical assignments and lab exercises to exercise your learning . Towards the end of the course, you will be working on a project where you be expected to create a project based on your learning . Our support team is available to help through email, phone or Live Support for any help you require during Lab and Project work.

Get Certified & Job Assistance

This course is designed for clearing **SAS Certified Base Programmer certification, Intellipaat R, Mahout,SAS Certified Base Programmer and the Intellipaat Course Completion certificate.**

At the end of the course there will be a quiz and project assignments once you complete them you will be awarded with Intellipaat Course Completion certificate.

Intellipaat enjoys strong relationships with multiple staffing companies in US, UK and have +80 clients across the globe. If you are looking out for exploring job opportunities, you can pass your resumes once you complete the course and we will help you with job assistance. We don’t charge any extra fees for passing the resume to our partners and clients.
Flexible Schedule

For Online Classroom training we provide flexible schedule. If you miss any session or you are not able to join the classes for the enrolled batch then you can reschedule your enrollment and join another batch or attend only the missed classes in another batch.

Lifetime free upgrade

Intellipaat courses come with lifetime free upgrade to latest version. It’s a lifetime investment in the skills you want to enhance

24 x 7 Lifetime Support & Access

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Superb LearningTook this course a week ago and I am impressed with the methodology used. Instructor is superb, and video quality is great. The topic explanations are easy-to-understand. Being from PHP background, it was difficult for me to enter into Data Science field, but this course certainly gave the answers to all my problems. Great job Intellipaat!!!

Well-designed QuizzesWhat I liked the most about this course is that the Quizzes are well designed to test conceptual understanding. I went through the sessions multiple times so as to clear the quizzes. It gave my Data Science career a great start as most important topics are compiled in just one training course. Had an excellent training experience.