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Data Science with R Training in Chennai

Greens Technologys located in Adyar and OMR provides Data Science with R training in Chennai. Become a certified Data Scientist by Learning R, SQL and Excel. Learn analytics from data manipulation to predictive modeling - using R. Get certified in 6 weeks.

In this R Certification Training, you’ll become an expert in analytics techniques using the R data science tool. R Training institute in Chennai for Data Science offers a comprehensive learning foundation that you can build your analytics career on.

Become an expert in data analytics using the R programming language in this data science certification training course.

About The Trainer

- Karthik is an experienced statistician and data miner with more than 10+ years of experience using R, Python and SAS and a passion for building analytical solutions. He is a M.S. in Quantitative Economics and Applied Mathematics graduate who has analytics experience working with companies like Capital One, Walmart, ICICI Lombard etc.

Karthik is a lead Data Scientist at Citi Bank. As a Certified Predictive Modeler, Statistical Business Analyst, and Certified Advanced Programmer, Karthik is passionate about sharing his knowledge on how data science can support data-driven business decisions.

Flexible Timings / Weekend classes Available.

Talk to the Trainer @ +91-8939915572

Data Science with R Training courses in Chennai

  1. Introduction to Data Science with R
  2. Scientific Distributions Used in R for Data Science
  3. Machine Learning
  4. Practical Applications of Machine Learning

Data Science with R Training Course Content

Learn to use R as your Data Science tool of choice This course teaches you R as a tool for data science, and specifically for implementing an advanced Machine Learning algorithm with R

  • 2 HADOOP
  • 3 Spark
  • 4 Statistics + Machine Learning
  • 5 Python

  • Introduction to Data Science

    • Need for Data Scientists
    • Foundation of Data Science
    • What is Business Intelligence
    • What is Data Analysis
    • What is Data Mining
    • What is Machine Learning
    • Analytics vs Data Science
    • Value Chain
    • Types of Analytics
    • Lifecycle Probability
    • Analytics Project Lifecycle


    • Basis of Data Categorization
    • Types of Data
    • Data Collection Types
    • Forms of Data & Sources
    • Data Quality & Changes
    • Data Quality Issues
    • Data Quality Story
    • What is Data Architecture
    • Components of Data Architecture
    • OLTP vs OLAP
    • How is Data Stored?

    Big Data

    • What is Big Data?
    • 5 Vs of Big Data
    • Big Data Architecture
    • Big Data Technologies
    • Big Data Challenge
    • Big Data Requirements
    • Big Data Distributed Computing & Complexity
    • Hadoop
    • Map Reduce Framework
    • Hadoop Ecosystem

    Data Science Deep Dive

    • What Data Science is
    • Why Data Scientists are in demand
    • What is a Data Product
    • The growing need for Data Science
    • Large Scale Analysis Cost vs Storage
    • Data Science Skills
    • Data Science Use Cases
    • Data Science Project Life Cycle & Stages
    • Map Reduce Framework
    • Hadoop Ecosystem
    • Data Acuqisition
    • Where to source data
    • Techniques
    • Evaluating input data
    • Data formats
    • Data Quantity
    • Data Quality
    • Resolution Techniques
    • Data Transformation
    • File format Conversions
    • Annonymization

    Intro to R Programming

    • Introduction to R
    • Business Analytics
    • Analytics concepts
    • The importance of R in analytics
    • R Language community and eco-system
    • Usage of R in industry
    • Installing R and other packages
    • Perform basic R operations using command line
    • Usage of IDE R Studio and various GUI

    R Programming Concepts

    • The datatypes in R and its uses
    • Built-in functions in R
    • Subsetting methods
    • Summarize data using functions
    • Use of functions like head(), tail(), for inspecting data
    • Use-cases for problem solving using R

    Data Manipulation in R

    • Various phases of Data Cleaning
    • Functions used in Inspection
    • Data Cleaning Techniques
    • Uses of functions involved
    • Use-cases for Data Cleaning using R

    Data Import Techniques in R

    • Import data from spreadsheets and text files into R
    • Importing data from statistical formats
    • Packages installation for database import
    • Connecting to RDBMS from R using ODBC and basic SQL queries in R
    • Web Scraping
    • Other concepts on Data Import Techniques

    Exploratory Data Analysis (EDA) using R

    • What is EDA?
    • Why do we need EDA?
    • Goals of EDA
    • Types of EDA
    • Implementing of EDA
    • Boxplots, cor() in R
    • EDA functions
    • Multiple packages in R for data analysis
    • Some fancy plots
    • Use-cases for EDA using R

    Data Visualization in R

    • Story telling with Data
    • Principle tenets
    • Elements of Data Visualization
    • Infographics vs Data Visualization
    • Data Visualization & Graphical functions in R
    • Plotting Graphs
    • Customizing Graphical Parameters to improvise the plots
    • Various GUIs
    • Spatial Analysis
    • Other Visualization concepts

    Big Data and Hadoop Introduction

    • What is Big Data and Hadoop?
    • Challenges of Big Data
    • Traditional approach Vs Hadoop
    • Hadoop Architecture
    • Distributed Model
    • Block structure File System
    • Technologies supporting Big Data
    • Replication
    • Fault Tolerance
    • Why Hadoop?
    • Hadoop Eco-System
    • Use cases of Hadoop
    • Fundamental Design Principles of Hadoop
    • Comparison of Hadoop Vs RDBMS

    Understand Hadoop Cluster Architecture

    • Hadoop Cluster & Architecture
    • 5 Daemons
    • Hands-On Exercise
    • Typical Workflow
    • Hands-On Exercise
    • Writing Files to HDFS
    • Hands-On Exercise
    • Reading Files from HDFS
    • Hands-On Exercise
    • Rack Awareness
    • Before Map Reduce

    Map Reduce Concepts

    • Map Reduce Concepts
    • What is Map Reduce?
    • Why Map Reduce?
    • Map Reduce in real world.
    • Map Reduce Flow
    • What is Mapper?
    • What is Reducer?
    • What is Shuffling?
    • Word Count Problem
    • Hands-On Exercise
    • Distributed Word Count Flow & Solution
    • Log Processing and Map Reduce
    • Hands-On Exercise

    Advanced Map Reduce Concepts

    • What is Combiner?
    • Hands-On Exercise
    • What is Partitioner?
    • Hands-On Exercise
    • What is Counter?
    • Hands-On Exercise
    • InputFormats/Output Formats
    • Hands-On Exercise
    • Map Join using MR
    • Hands-On Exercise
    • Reduce Join using MR
    • Hands-On Exercise
    • MR Distributed Cache
    • Hands-On Exercise
    • Using sequence files & images with MR
    • Hands-On Exercise
    • Planning for Cluster & Hadoop 2.0 Yarn
    • Configuration of Hadoop
    • Choosing Right Hadoop Hardware?
    • Choosing Right Hadoop Software?
    • Hadoop Log Files?

    Hadoop 2.0 & YARN

    • Hadoop 1.0 Challenges
    • NN Scalability
    • NN SPOF & HA
    • Job Tracker Challenges
    • Hadoop 2.0 New Features
    • Hadoop 2.0 Cluster Architecture & Federation
    • Hadoop 2.0 HA
    • Yarn & Hadoop Ecosystem
    • Yarn MR Application Flow


    • Introduction to Pig
    • What Is Pig?
    • Pig’s Features & Pig Use Cases
    • Interacting with Pig
    • Basic Data Analysis with Pig
    • Hands-On Exercise
    • Pig Latin Syntax
    • Loading Data
    • Hands-On Exercise
    • Simple Data Types
    • Field Definitions
    • Data Output
    • Viewing the Schema
    • Hands-On Exercise
    • Filtering and Sorting Data
    • Hands-On Exercise
    • Commonly-Used Functions
    • Hands-On Exercise: Pig for ETL Processing
    • Processing Complex Data with Pig
    • Hands-On Exercise
    • Storage Formats
    • Complex/Nested Data Types
    • Hands-On Exercise
    • Grouping
    • Hands-On Exercise
    • Built-in Functions for Complex Data
    • Hands-On Exercise
    • Iterating Grouped Data
    • Hands-On Exercises
    • Multi-Dataset Operations with Pig
    • Hands-On Exercise
    • Techniques for Combining Data Sets

    Module 7

    • Joining Data Sets in Pig
    • Hands-On Exercise
    • Splitting Data Sets
    • Hands-On Exercise


    • Hive Fundamentals & Architecture
    • Loading and Querying Data in Hive
    • Hands-On Exercise
    • Hive Architecture and Installation
    • Comparison with Traditional Database
    • HiveQL: Data Types, Operators and Functions,
    • Hands-On Exercise
    • Hive Tables ,Managed Tables and External Tables
    • Hands-On Exercise
    • Partitions and Buckets
    • Hands-On Exercise
    • Storage Formats, Importing Data, Altering Tables, Dropping Tables
    • Hands-On Exercise
    • Querying Data, Sorting and Aggregating, Map Reduce Scripts,
    • Hands-On Exercise


    • Joins & Sub queries, Views
    • Hands-On Exercise
    • Integration, Data manipulation with Hive
    • Hands-On Exercise
    • User Defined Functions,
    • Hands-On Exercise
    • Appending Data into existing Hive Table
    • Hands-On Exercise
    • Static partitioning vs dynamic partitioning
    • Hands-On Exercise


    • CAP Theorem
    • HBase Architecture and concepts
    • Introduction to HBase
    • Client API’s and their features
    • HBase tables The ZooKeeper Service
    • Data Model, Operations


    • Programming and Hands on Exercises


    • Introduction to Sqoop
    • MySQL Client & server
    • Connecting to relational data base using Sqoop
    • Importing data using Sqoop from Mysql
    • Exporting data using Sqoop to MySql
    • Incremental append
    • Importing data using Sqoop from Mysql to hive
    • Exporting data using Sqoop to MySql from hive
    • Importing data using Sqoop from Mysql to hbase
    • Using queries and sqoop

    Flume & Oozie

    • What is Flume?
    • Why use Flume, Architecture, configurations
    • Master, collector, Agent
    • Twitter Data Sentimental Analysis project
    • Oozie
    • What is Oozie, Architecture, configurations?
    • Oozie Job Submission
    • Oozie properties
    • Hands on exercises


    • Social Media Final Project
    • Hadoop Project
    • Objective
    • Problem Definition
    • Solution
    • Discuss data sets and specifications of the project.

    Project in Healthcare Domain

    • Hadoop Project in Healthcare
    • Objective
    • Problem Definition
    • Solution
    • Discuss data sets and specifications of the project.

    Project in Finance/Banking Domain

    • Hadoop Project in Banking Domain
    • Objective
    • Problem Definition
    • Solution
    • Discuss data sets and specifications of the project.


    Apache Spark

    • Introduction to Apache Spark
    • Why Spark
    • Batch Vs. Real Time Big Data Analytics
    • Batch Analytics – Hadoop Ecosystem Overview,
    • Real Time Analytics Options,
    • Streaming Data – Storm,
    • In Memory Data – Spark, What is Spark?,
    • Spark benefits to Professionals
    • Limitations of MR in Hadoop
    • Components of Spark
    • Spark Execution Architecture
    • Benefits of Apache Spark
    • Hadoop vs Spark

    Introduction to Scala

    • Features of Scala
    • Basic Data Types of Scala
    • Val vs Var
    • Type Inference
    • REPL
    • Objects & Classes in Scala
    • Functions as Objects in Scala
    • Anonymous Functions in Scala
    • Higher Order Functions
    • Lists in Scala
    • Maps
    • Pattern Matching
    • Traits in Scala
    • Collections in Scala

    Spark Core Architecture

    • Spark & Distributed Systems
    • Spark for Scalable Systems
    • Spark Execution Context
    • What is RDD
    • RDD Deep Dive
    • RDD Dependencies
    • RDD Lineage
    • Spark Application In Depth
    • Spark Deployment
    • Parallelism in Spark
    • Caching in Spark

    Spark Internals

    • Spark Transformations
    • Spark Actions
    • Spark Cluster
    • Spark SQL Introduction
    • Spark Data Frames
    • Spark SQL with CSV
    • Spark SQL with JSON
    • Spark SQL with Database

    Spark Streaming

    • Features of Spark Streaming
    • Micro Batch
    • Dstreams
    • Transformations on Dstreams
    • Spark Streaming Use Case

    Statistics + Machine Learning


    Whats is Statistics

    • Descriptive Statistics
    • Central Tendency Measures
    • The Story of Average
    • Dispersion Measures
    • Data Distributions
    • Central Limit Theorem
    • What is Sampling
    • Why Sampling
    • Sampling Methods
    • Inferential Statistics
    • What is Hypothesis testing
    • Confidence Level
    • Degrees of freedom
    • what is pValue
    • Chi-Square test
    • What is ANOVA
    • Correlation vs Regression
    • Uses of Correlation & Regression

    Machine Learning

    Machine Learning Introduction

    • ML Fundamentals
    • ML Common Use Cases
    • Understanding Supervised and Unsupervised Learning Techniques
    • Clustering
    • Similarity Metrics
    • Distance Measure Types: Euclidean, Cosine Measures
    • Creating predictive models
    • Understanding K-Means Clustering
    • Understanding TF-IDF, Cosine Similarity and their application to Vector Space Model
    • Case study
    • Implementing Association rule mining
    • Case study
    • Understanding Process flow of Supervised Learning Techniques
    • Decision Tree Classifier
    • How to build Decision trees
    • Case study
    • Random Forest Classifier
    • What is Random Forests
    • Features of Random Forest
    • Out of Box Error Estimate and Variable Importance
    • Case study
    • Naive Bayes Classifier.
    • Case study
    • Project Discussion
    • Problem Statement and Analysis
    • Various approaches to solve a Data Science Problem
    • Pros and Cons of different approaches and algorithms.
    • Linear Regression
    • Case study
    • Logistic Regression
    • Case study
    • Text Mining
    • Case study
    • Sentimental Analysis
    • Case study


    Getting Started with Python

    • Python Overview
    • About Interpreted Languages
    • Advantages/Disadvantages of Python pydoc.
    • Starting Python
    • Interpreter PATH
    • Using the Interpreter
    • Running a Python Script
    • Python Scripts on UNIX/Windows
    • Python Editors and IDEs.
    • Using Variables
    • Keywords
    • Built-in Functions
    • StringsDifferent Literals
    • Math Operators and Expressions
    • Writing to the Screen
    • String Formatting
    • Command Line Parameters and Flow Control.

    Sequences and File Operations

    • Lists
    • Tuples
    • Indexing and Slicing
    • Iterating through a Sequence
    • Functions for all Sequences
    • Using Enumerate()
    • Operators and Keywords for Sequences
    • The xrange() function
    • List Comprehensions
    • Generator Expressions
    • Dictionaries and Sets.

    Deep Dive – Functions Sorting Errors and Exception Handling

    • Functions
    • Function Parameters
    • Global Variables
    • Variable Scope and Returning Values. Sorting
    • Alternate Keys
    • Lambda Functions
    • Sorting Collections of Collections
    • Sorting Dictionaries
    • Sorting Lists in Place
    • Errors and Exception Handling
    • Handling Multiple Exceptions
    • The Standard Exception Hierarchy
    • Using Modules
    • The Import Statement
    • Module Search Path
    • Package Installation Ways.

    Regular Expressionsit’s Packages and Object Oriented Programming in Python

    • The Sys Module
    • Interpreter Information
    • STDIO
    • Launching External Programs
    • PathsDirectories and Filenames
    • Walking Directory Trees
    • Math Function
    • Random Numbers
    • Dates and Times
    • Zipped Archives
    • Introduction to Python Classes
    • Defining Classes
    • Initializers
    • Instance Methods
    • Properties
    • Class Methods and DataStatic Methods
    • Private Methods and Inheritance
    • Module Aliases and Regular Expressions.

    Debugging, Databases and Project Skeletons

    • Debugging
    • Dealing with Errors
    • Using Unit Tests
    • Project Skeleton
    • Required Packages
    • Creating the Skeleton
    • Project Directory
    • Final Directory Structure
    • Testing your Setup
    • Using the Skeleton
    • Creating a Database with SQLite 3
    • CRUD Operations
    • Creating a Database Object.

    Machine Learning Using Python

    • Introduction to Machine Learning
    • Areas of Implementation of Machine Learning
    • Why Python
    • Major Classes of Learning Algorithms
    • Supervised vs Unsupervised Learning
    • Learning NumPy
    • Learning Scipy
    • Basic plotting using Matplotlib
    • Machine Learning application

    Supervised and Unsupervised learning

    • Classification Problem
    • Classifying with k-Nearest Neighbours (kNN)


    • General Approach to kNN
    • Building the Classifier from Scratch
    • Testing the Classifier
    • Measuring the Performance of the Classifier.
    • Clustering Problem
    • What is K-Means Clustering
    • Clustering with k-Means in Python and an

    Application Example.

    • Introduction to Pandas
    • Creating Data Frames
    • GroupingSorting
    • Plotting Data
    • Creating Functions
    • Converting Different Formats
    • Combining Data from Various Formats
    • Slicing/Dicing Operations.

    Scikit and Introduction to Hadoop

    • Introduction to Scikit-Learn
    • Inbuilt Algorithms for Use
    • What is Hadoop and why it is popular
    • Distributed Computation and Functional Programming
    • Understanding MapReduce Framework Sample MapReduce Job Run.

    Hadoop and Python

    • PIG and HIVE Basics
    • Streaming Feature in Hadoop
    • Map Reduce Job Run using Python
    • Writing a PIG UDF in Python
    • Writing a HIVE UDF in Python
    • Pydoop and MRjob Basics.

    Python Project Work

    • Real world project


    Data Science with R Training Course description

    The Data Science Certification with R has been designed to give you in-depth knowledge of the various data analytics techniques that can be performed using R. The data science course is packed with real-life projects and case studies, and includes R CloudLab for practice.

    Mastering R and using its packages: The course covers PROC SQL, SAS Macros, and various statistical procedures like PROC UNIVARIATE, PROC MEANS, PROC FREQ, and PROC CORP. You will learn how to use SAS for data exploration and data optimization.

    Mastering advanced analytics techniques: The course also covers advanced analytics techniques like clustering, decision tree, and regression. The course covers time series, it's modeling, and implementation using SAS.

    As a part of the course, you are provided with 4 real-life industry projects on customer segmentation, macro calls, attrition analysis, and retail analysis.

    R Training Objectives

    • Gain an in-depth understanding of data science process, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
    • Install the required R environment and other auxiliary tools and libraries
    • Understand the essential concepts of R programming like data types, tuples, lists, dicts, basic operators, and functions.
    • Perform high-level mathematical computing using NumPy package and its large library of mathematical functions
    • Perform scientific and technical computing using SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave.
    • Perform data analysis and manipulation using data structures and tools provided in Pandas package
    • Gain expertise in machine learning using the Scikit-Learn package
    • Gain an in-depth understanding of supervised learning and unsupervised learning models like linear regression, logistic regression, clustering, dimensionality reduction, K-NN, and pipeline
    • Use Scikit-Learn package for natural language processing
    • Use matplotlib library of R for data visualization
    • Extract useful data from websites by performing web scrapping using R
    • Integrate R with Hadoop, Spark, and MapReduce

    Who should take this R Data Scientist course?

    There is an increasing demand for skilled data scientists across all industries that makes this course suitable for participants at all levels of experience. We recommend this data science training especially for the following professionals:

    • Analytics professionals who want to work with R
    • Software professionals looking for a career switch in the field of analytics
    • IT professionals  interested in pursuing a career in analytics
    • Graduates looking to build a career in Analytics and Data Science
    • Experienced professionals who would like to harness data science in their fields
    • Anyone with a genuine interest in the field of Data Science

    Looking to become a R Certified Data Scientist?

    Enrol in the R training for Data Science. Get R certification Become a Certified Data Scientist with our premium R question bank.

    Analytical talent is in high demand. Learn from the leader in analytics. With nearly 100 hours of learning content available, the R training institute in Chennai for Data Science offers a comprehensive foundation for data science success.

    Course advisor

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    Named by Onalytica as one of the three most influential people in Big Data, Also an author for a number of leading Big Data and Data Science websites, including Datafloq, Data Science Central, and The Guardian. She also regularly speaks at renowned events.

    What is a Data Scientist?

    Data scientists are a new breed of analytical data expert who have the technical skills to solve complex problems – and the curiosity to explore what problems need to be solved.

    The biggest benefits of getting R certified is how it opens doors to employment. R certification demonstrates that you can learn your job more quickly.

    Typical job duties for data scientists

    There's not a definitive job description when it comes to a data scientist role. But here are a few things you'll likely be doing:

    • Collecting large amounts of unruly data and transforming it into a more usable format.
    • Solving business-related problems using data-driven techniques.
    • Working with a variety of programming languages, including SAS, R and R.
    • Having a solid grasp of statistics, including statistical tests and distributions.
    • Staying on top of analytical techniques such as machine learning, deep learning and text analytics.
    • Communicating and collaborating with both IT and business.
    • Looking for order and patterns in data, as well as spotting trends that can help a business’s bottom line.

    What’s in a data scientist’s toolbox?

    These terms and technologies are commonly used by data scientists:

    • Data visualization: the presentation of data in a pictorial or graphical format so it can be easily analyzed.
    • Machine learning: a branch of artificial intelligence based on mathematical algorithms and automation.
    • Deep learning: an area of machine learning research that uses data to model complex abstractions.
    • Pattern recognition: technology that recognizes patterns in data (often used interchangeably with machine learning).
    • Data preparation: the process of converting raw data into another format so it can be more easily consumed.
    • Text analytics: the process of examining unstructured data to glean key business insights.

    How can you become a data scientist?

    Positioning yourself for a career in data science could be a smart move. You’ll have plenty of job opportunities, plus it’s a chance to work in the technology field with room for experimentation and creativity. So what’s your strategy?

    If you’re a student

    Choosing Greens Technologies, Best R training institute in Chennai that offers data science and analytics training.

    If you’re a professional who wants to shift careers

    While most data scientists have backgrounds as data analysts or statisticians, others come from non-technical fields such as business or economics. How can professionals from such diverse backgrounds end up in the same field? It’s important to look at what they have in common: a knack for solving problems, the ability to communicate well and an insatiable curiosity about how things work. Learn how the R training institute in Chennai for Data Science gives you the tools to become a certified data scientist.

    you’ll also need a solid understanding of:

    • Statistics and machine learning.
    • Coding languages such as SAS, R or Python.
    • Databases such as Oracle and Postgres.
    • Data visualization and reporting technologies.
    • Hadoop and MapReduce.

      • If you don’t want to learn these skills on your own, take an online course or R class room training in Greens Technologys . And then, of course, you should network. Connect with other data scientists in your company, or find an online community. They’ll give you insider information into what data scientists do – and where you’ll find the best jobs.

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    Data Science with R training in Chennai Reviews

    Greens Technology Reviews given by our students already completed the training with us. Please give your feedback as well if you are a student.

    Data Science with R training in Chennai Reviews from our Students

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    Friends I’m from SQL background with 8+ years of experience, I had planned to move into Analytics department, when I was looking for various training institutes to take course on R with Data Science I came to know about Greens Technology Adayar and Karthik who is the course instructor. The way he took sessions was inspiring us to learn further in R and machine Learning. No wonder with such intellect his class did wonders to us, I even got great insights from him regarding data scientist job interviews. His class and materials which he shared is of great knowledge base. Using those materials and capstone projects I could clear interviews and I’m a data scientist for almost two years. This move was defining moment for a better change in my career.

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    I attended the Base R and Advanced R course class room sessions. The outline of the each course were well prepared and presented using latest video technology. The instructor is very talented and expert on Analytics concepts both theoretically and practically. I would highly recommend this institute to any one who wants to learn R ." I joined "Greens Technology" because of their proven expertise in R practical training. Here, I learnt the Magic of R . The constant and personal interaction with the Trainer, Live Projects, Certification Training and Study material are the best part. The trainers are extremely proficient in their knowledge and understanding of the topics. The instructors I had were both skillful and possessed the knowledge required to present the material to the classes. The R Certification training program has provided me with the necessary skill sets to prepare me for the corporate world. "Greens Technology" is the stepping stone to my success in the IT world. The money invested is well worth the reward. On my personal experience I recommend "Greens Technology" heart fully as the best training institute for IT Business Intelligence education. Thank you "Greens Technology" for helping me achieve my dream of becoming an R Certified Professional.

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    "The course delivery certainly is much better than what I expected. I am glad that I decided to choose Greens Technology for the R course. Wonderful learning experience and I like the way classes are organized and good support staff. Greens Technology provides quality learning experience within affordable price. Also thanks to my educator Dinesh , his teaching inspires and motivates to learn..

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    "Friends I am from Manual testing background having 6+ years experienced. I planned to Move into R Business Intelligence (BI) . I Came to know about Greens technologies and Sai who is working in CTS . They Really helped me to clear the interview. Thanks to Sai Sir. Knowledgeable Presenters, Professional Materials, Excellent Support" what else can a person ask for when acquiring a new skill or knowledge to enhance their career. Greens Technology true to its name is the place to gather,garner and garden the knowledge for all around the globe. My Best wishes to Greens Technology team for their upcoming bright future in E-Learning sector.

    R Training Venue:

    Are you located in any of these areas - Adyar, Mylapore, Nandanam, Nanganallur, Nungambakkam, OMR, Pallikaranai, Perungudi, Ambattur, Aminjikarai, Adambakkam, Anna Nagar, Anna Salai, Ashok Nagar, Besant Nagar, Choolaimedu, Chromepet, Medavakkam, Porur, Saidapet, Sholinganallur, St. Thomas Mount, T. Nagar, Tambaram, Teynampet, Thiruvanmiyur, Thoraipakkam,Vadapalani, Velachery, Egmore, Ekkattuthangal, Guindy, K.K.Nagar, Kilpauk, Kodambakkam, Madipakkam, Villivakkam, Virugambakkam and West Mambalam.

    Our Adyar office is just few kilometre away from your location. If you need the best R training in Chennai, driving couple of extra kilometres is worth it!

    Data Science with R Related Training Courses in Chennai

    best R training center in chennai "Karthik! I am really delighted about the R course and i am surprised to see the depth of your knowledge in all aspects of the SAS. I see that many statistician with over 15+ yrs experience doesn't have the knowledge that you have. I really enjoyed your sessions, definitely look forward to learn more from you in the future. Thanks again."

    R training chennai ""Dear Karthik, R training has been outstanding. You have covered every aspect of the R which would boost the confidence of the attendee to dive into greater depths and face the interviews subsequently. I feel confident after attending the R course. I am sure you would be providing us your valuable high level guidence in our initial realtime project . Each of your session is a eye opener and it is a great joy to attend your R training. Thanks and Kindest Regards ""

    R training classes in chennai "I thought I knew R until I took this course. My company sent me here against my will. It was definitely worth and I found out how many things I was doing wrong. Karthik is awesome. but i got a lot inspired by you. I will keep in touch and will always try to learn from you as much as I can. Thanks once again Karthik"

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