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R Programming for Data Science Training

R Programming for Data Science Training

About R Programming for Data Science Training Course

EnhanceLearn's R Programming Training for Data Science gives a clear understanding of the core concepts like regression, statistical computing, data analytics, machine learning algorithms, data visualization, data mining using RStudio for effective data analysis and master in Data Manipulation with R programming. Our data science expert trains on projects to learn data analytics with R.

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About Course

What is R Programming for Data Science?

Learn R programming language that is deployed for different purposes like graphics representation, statistical analysis and reporting. You will learn about the various functions, data structures, variables and flow of control. The Data Science with R has been designed to deliver you in-depth knowledge of the various data analytics techniques that can be performed using R.

  • Mastering R language: The data science course provides an in-depth understanding of the R language, R-studio, and R packages. You will learn the various types of apply functions including DPYR, gain an understanding of data structure in R, and perform data visualizations using the various graphics available in R.
  • Mastering advanced statistical concepts: The data science training course also includes various statistical concepts such as linear and logistic regression, cluster analysis and forecasting. You will also learn hypothesis testing.

What does R programmer do?

An R programmer works on a language called R. R programming is used for statistical computing and data collection. The aim is to analyse the goal of optimization for an organization or business. R programmer also involves in designing statistical models, formulating procedures, and providing technical assistance for clients.

Why you should take R Programming training?

Learn R programming language, which is a language also provides an environment for statistical computing and graphics of data. 

R programming is a statistical language for Data Science specialization that is finding higher adoption rates today thanks to its extensible nature. It can be widely deployed for various applications and can be easily scaled. 

This R training to learn R tool will help you apply many jobs as the R programming for Data Science is in very much demand.

Jobs that are for R programming tools are being created produced at big companies offering very good pay scales.

What you will learn in this R Programming Certification Training?

  • Collect detailed information using R profiler
  • learn to use and navigate in the RStudio interface
  • Learn Data Science concepts of R and functioning of R-Calculator
  • perform basic commands in the R programming language
  • Make use of R loop functions and debugging tools
  • Understand various functions like Stack, Merge and Strsplit
  • Learn to create Pie charts, plots and vectors
  • Assign value to variables, generate repeat and factor levels
  • Performing sorting, analyse variance and the cluster
  • ODBC Tables reading, linear and logistic regression
  • Understand database connectivity and critical programming language concepts
  • Deploy R programming for Hadoop applications

Who should take this R Programming Course for Data Scientist Training ?

  • Software engineers, data analysts and Business intelligence professionals
  • Fresh Graduate from college and Looking for career in IT world
  • If QA professionals interested to polish their skills and knowledge
  • Entry Level or Professionals aspiring for a career in data science
  • Everybody interested in statistics and data sciences


  • No skills requirement is necessary.
  • Entry Level Graduate or any Jobseeker willing to become a Data Scientist can apply
  • Programming skills can be a helpful
  • Good understanding of basic statistical concepts and a strong quantitative background.
  • Knowledge of any scripting languages such as Java, Perl, Python or R and SQL is a plus

R Programming Course Highlights

  • Learn r programming and obtain hands-on experience in RStudio
  • Doing R integration with Hadoop through practical R exercises
  • Master in Data Manipulation with R programming, Data visualization, computational analytics and graphical data analysis.
  • Practice on software tools to gain hands-on expertise.
  • Work on real time project related situations and examples to provide you the feel of real work environment.
  • R programming Course Projects for Data Science concepts
  • Group discussions, Mock interview sessions, and Interview questions to prepare you to attend interviews with confidence.
  • Access to instructor through email to address any questions.

How EnhanceLearn Training can help you

  • 48 Hours of hands-on session per batch and once enrolled you can take any number of batches for 90 days
  • 24x7 Expert Support and GTA (Global Teaching Assistant, SME) support available even to schedule a one on one session for doubt clearing
  • Project Based learning approach with evaluation after each module
  • Project Submission mandatory for Certification and thoroughly evaluated
  • 3 Months Experience Certificate on successful project completion
  • We provide you with the Cloud Lab environment to practice your practical.

For becoming an R programming expert, choose our best Training and Placement Program. If you are interested in joining the EnhanceLearn team, please email at

Course Curriculum

Module 1: Introduction to R

  • Overview and Importance of R
  • Data Types and Variables in R
  • Operators in R
  • R Script
  • Knowledge Check
  • R language for statistical programming
  • Introduction to R Studio
  • The statistical packages
  • Familiarity with different data types and functions
  • Use SQL to apply ‘join’ function
  • Components of R Studio
  • Visualization and debugging tools
  • Learn about R-bind

Module 2: 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
  • Variables values assignment

Module 3: Sorting Dataframe

  • R functionality
  • Rep Function
  • Generating Repeats
  • Sorting and generating Factor Levels
  • Transpose
  • Stack Function
  • Introduction to matrix and vector in R
  • Understanding the various functions
    • 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
  • 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 Simp0le 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
  • 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
  • What is ROC
  • Detailed understanding of ROC
  • Area under ROC Curve
  • Converting the variable
  • Data set partitioning
  • Understanding how to check for multicollinearity
  • How two or more variables are highly correlated
  • Building of model
  • Advanced data set partitioning
  • Interpreting of the output
  • Predicting the output
  • Confusion matrix
  • Detailed confusion matrix
  • Understanding sensitivity and specificity
  • A graphical plot illustrating binary classifier system
  • Deploying the Hosmer-Lemeshow test
  • 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


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R Programming for Data Science Training
R Programming Certificate
Job Overview
Key Features
Training FAQs
What is Data Science?

Data Science is a multidisciplinary field that utilizes scientific approaches, algorithms and framework to excerpt knowledge and visions from data in different forms, structures and unstructured data. It is a combination of different tools, algorithms, and machine learning philosophies having an objective of finding the hidden patterns from the raw information. 

What does R programmer do?
Difference: Data Analyst vs Data Scientist?
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Raj Saxena 

I completed my R programming training, thanks to EnhanceLearn. I personally feel that EnhanceLearn is the right place to embark on a successful R programming career with this course. I shall definitely recommend the EnhanceLearn training services to my friends and colleagues.

Aabha Rau 
Well prepared course!

Thank you EnhanceLearn for providing such a great R programming training course. I Must recommend this course to all the people who are looking a future in Data science. Great job of covering the all the tasks and topics in easy to follow rhythm.

Mohit Arora 
Best training by EnhanceLearn.

EnhanceLearn provides the BEST training and placement to students. I did R programming Training and trainers assisted me in this module until the end of the training. They also provided best placement assistance. I wish I could give many stars but only 5 stars I could give.

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