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Understand key principles of R programming. Join today to learn from an R programming expert.

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Course Duration

25 Hrs.

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1 Project

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  • Croma Campus is a leading R programming training institute that offers the best R programming training to students looking to get a secured job in an MNC and corporate giant.
    • R users grow by about 40%, and organizations are using it in their day-to-day activities.

      Hiring companies include Google, Bing, Facebook, Uber, Twitter, and Ford.

      Being used for purposes like cleaning the data, forecasting, creating mind-blowing visualizations.

      R is a highly demanded data analytics skill among all.

      Get hands-on R programming training and become a certified professional.

  • Our R Programming Training at Croma Campus is mainly aimed at becoming an expert in the R language. So, what are you waiting for Simply enrol for the best R Programming certification course and get a secured job without investing much.

R Programming Training

About-Us-Course

  • No matter whether you are a professional or a novice, our R Programming training course may help you learn all the skills required and let you implement them at work.
    • Master the basics of data analysis by manipulating common data structures such as vectors, matrices, and data frames.

      Learn to use R to clean, analyse, and visualize data.

      Learn how to use GitHub to manage data science projects.

      Perform regression analysis, least squares and inference using regression models.

      Learn the most popular tool for data analytics.

  • So, what are you waiting for simply enrol for the best R Programming training course and learn all the skills to outperform others at the time of interview.

  • R programming is one of the fastest-growing languages and has undergone a successful span of more than 25 years as far as its adoption is concerned. This success also reveals a promising future scope of r programming language.
    • Glassdoor has ranked Data Science with a median base salary of $110000 and 4,524 job openings.

      It is the fastest-growing tech employment area today.

      Job profiles include Data Scientist, Analytics Manager, Database Administrator, Data Engineer, etc.

      Your salary will increase as you gain knowledge and experience.

  • Upon the completion of your R Programming training course, your career will grow like wildfire. With attractive salary packages and designation, your career will skyrocket and get ahead by standing tall above the rest.
    • You will get a better knowledge of programming and how to implement it.

      Enhanced knowledge of the web development framework to develop dynamic websites swiftly.

      Increased chances of working in leading software companies like Infosys, Wipro, Amazon, TCS, IBM and many more.

      Learn how to design, develop, test, support and deploy desktop, custom web, and mobile applications.

      Design and improve testing and maintenance activities and procedures.

  • R ultimately established to be an inexpensive, Custom-Made, and inflatable statistical analysis solution. R has lots of packages/libraries that contain numerous helpful functions that you can employ for your use. R is considered as an abundance who are all getting into the analytics field.
    • Nearly 40% started a new career after completing this specialization.

      Nearly 20% got a pay increase or promotion.

      R has become the preferred computing environment for the statistical community.

      The number of R users enlarges by 40%

  • After the completion of your course, you will need to perform a multitude of job roles and responsibilities at work. Here are a few of them;
    • Development, automate and review technical architecture, requirements and statistical models.

      Coordinate with end users for designing and implementation of analytics solutions as per the needs of the project.

      Participate in team problem-solving efforts and offer ideas to solve client issues.

      Design of experiments/data collection for statistical analysis.

      Program code of qualitative variable and manipulation tasks with various statistical methodologies

      Continually look for ways to improve the overall performance of systems.

  • Our R Programming training course is suggested for all those novices and specialists who are keen on working in the analytics industry. R is now well thought out to be not just the most popular open-source analytic tool but the most popular analytics tool in the world.
    • There is always an adequate job opportunity for R programming.

      Some of the top hiring industries in R programming include Infosys, Spectra, IBM, TCS, QA Infotech, and more.

      R is suitable for all IT professionals like Big Data analytics, Business analytics, scientific research

      There is also insufficiency in analytics for professionals with R programming skills.

  • When you finish every course and complete the hands-on project, you'll earn a Certificate that you can share with prospective employers and your professional network.
    • Our certification is accredited worldwide.

      Increases the value of your resume and you can attain leading job posts.

      Get noticed by the potential recruiters, and get hired right away.

      Our experts also support you by providing interview tips.

Why Should You Learn R Programming?

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25 Hrs.
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CURRICULUM & PROJECTS

R Programming Training

    This course is suggested for all those novices and specialists who are keen on working in the analytics industry. R is suitable for all IT professionals like Big data analytics, Business analytics, scientific research, statistical reporting, Econometrics, social science, business intelligence, and business development. Researchers who perform data analysis with higher dimensional graphs. Students who need R for their courses. Prerequisite It is not necessary to have any prior knowledge of R. R is now well thought out to be not just the most popular open-source analytic tool but the most popular analytics tool in the world

    In this program you will learn:

    • Overview :
    • R Programming Basics.
    • Variable types and data structures in base R.
    • Getting data into the R environment.
    • Data frame manipulation.
    • Handling dates in R.
    • Exploratory Data Analysis (Descriptive Statistics).
    • Working with text data.
    • Control flow & functions.
    • Graphics in R Overview.
    • Advanced R graphics.
    • Inferential Statistics.
    • General Linear Regression Models in R.
Get full course syllabus in your inbox

    Overview:

    • History of R
    • Advantages and disadvantages
    • Downloading and installing
    • How to find documentation
Get full course syllabus in your inbox

    R Programming Basics:

    • Using the R console and R Studio
    • Getting help
    • Learning about the environment
    • Writing and executing scripts
    • Object oriented programming
    • Introduction to vectorised calculations
    • Introduction to data frames
    • Installing and loading packages
    • Working directory
    • Saving your work
Get full course syllabus in your inbox

    Variable types and data structures in base R:

    • Variables and assignment
    • Data types
    • Numeric, character, Boolean, and factors
    • Data structures
    • Vectors, matrices, arrays, data frames, lists
    • Indexing, sub-setting
    • Assigning new values
    • Viewing data and summaries
    • Naming conventions
    • Objects
Get full course syllabus in your inbox

    Getting data into the R environment:

    • Built-in data
    • Reading data from structured text files
    • Reading data using ODBC
Get full course syllabus in your inbox

    Data frame manipulation:

    • Introduction to tables, enhanced data frames
    • Renaming columns
    • Adding new columns
    • Binning data (continuous to categorical)
    • Combining categorical values
    • Transforming variables
    • Handling missing data
    • Merging datasets together
    • Stacking datasets together (concatenation)
Get full course syllabus in your inbox

    Handling dates in R:

    • Date and date-time classes in R
    • Formatting dates for modeling
Get full course syllabus in your inbox

    Exploratory Data Analysis (Descriptive Statistics):

    • Continuous data
    • Distributions
    • Quantiles, mean
    • Bi-modal distributions
    • Histograms, box-plots
    • Categorical data
    • Tables
    • Bar plots
    • Group by calculations
    • Split-apply-combine
    • Reshaping and pivoting data in R (long to wide with aggregation)
    • Melt and cast
Get full course syllabus in your inbox

    Working with text data:

    • Finding and matching patterns in text
    • Stringer package for text manipulation
    • Introduction to regular expressions in R
    • Categorical data wrangling with forcats
Get full course syllabus in your inbox

    Control flow & functions:

    • Truth testing
    • Branching
    • Looping
    • Functions
    • Parameters
    • Return values
    • Variable scope
    • Exception handling
    • Applying functions across dimensions
    • Sapply, lapply, apply
    • Programming with map and purr
Get full course syllabus in your inbox

    Graphics in R Overview:

    • Base graphics system in R
    • Scatterplots, histograms, bar charts, box and whiskers, dot plots
    • Labels, legends, titles, axes
    • Exporting graphics to different formats
Get full course syllabus in your inbox

    Advanced R graphics:

    • Understanding the grammar of graphics
    • Quick plots (qplot function)
    • Building graphics by pieces (ggplot function)
    • Understanding geoms (geometries)
    • Linking chart elements to variable values
    • Controlling legends and axes
    • Exporting graphics
Get full course syllabus in your inbox

    Inferential Statistics:

    • Bivariate correlation
    • T-test and non-parametric equivalents
    • Chi-squared test
Get full course syllabus in your inbox

    General Linear Regression Models in R:

    • Understanding formulas
    • Linear and logistic regression models
    • Regression plots
    • Confounding / interaction in regression
    • Evaluating residuals
    • Scoring new data from models (prediction)
    • Useful plots from regression models
Get full course syllabus in your inbox

    Placement Guide:

    • Tips to clear an Interview
    • Common Interview questions and answers
    • R programming Interview Questions and Answers
    • Resume Building Guide
    • Attempt for the related Global Certification Exam
    • Earn Credentials and Start applying for Jobs
Get full course syllabus in your inbox

    History of R

    Advantages and disadvantages

    Downloading and installing

    How to find documentation

Get full course syllabus in your inbox

    Using the R console and R Studio

    Getting help

    Learning about the environment

    Writing and executing scripts

    Object oriented programming

    Introduction to vectorised calculations

    Introduction to data frames

    Installing and loading packages

    Working directory

    Saving your work

Get full course syllabus in your inbox

    Variables and assignment

    Data types

    Numeric, character, Boolean, and factors

    Data structures

    Vectors, matrices, arrays, data frames, lists

    Indexing, sub-setting

    Assigning new values

    Viewing data and summaries

    Naming conventions

    Objects

Get full course syllabus in your inbox

    Built-in data

    Reading data from structured text files

    Reading data using ODBC

Get full course syllabus in your inbox

    Introduction to tables, enhanced data frames

    Renaming columns

    Adding new columns

    Binning data (continuous to categorical)

    Combining categorical values

    Transforming variables

    Handling missing data

    Merging datasets together

    Stacking datasets together (concatenation)

Get full course syllabus in your inbox

    Date and date-time classes in R

    Formatting dates for modeling

Get full course syllabus in your inbox

    Continuous data

    Distributions

    Quantiles, mean

    Bi-modal distributions

    Histograms, box-plots

    Categorical data

    Tables

    Bar plots

    Group by calculations

    Split-apply-combine

    Reshaping and pivoting data in R (long to wide with aggregation)

    Melt and cast

Get full course syllabus in your inbox

    Finding and matching patterns in text

    Stringer package for text manipulation

    Introduction to regular expressions in R

    Categorical data wrangling with forcats

Get full course syllabus in your inbox

    Truth testing

    Branching

    Looping

    Functions

    Parameters

    Return values

    Variable scope

    Exception handling

    Applying functions across dimensions

    Sapply, lapply, apply

    Programming with map and purr

Get full course syllabus in your inbox

    Base graphics system in R

    Scatterplots, histograms, bar charts, box and whiskers, dot plots

    Labels, legends, titles, axes

    Exporting graphics to different formats

Get full course syllabus in your inbox

    Understanding the grammar of graphics

    Quick plots (qplot function)

    Building graphics by pieces (ggplot function)

    Understanding geoms (geometries)

    Linking chart elements to variable values

    Controlling legends and axes

    Exporting graphics

Get full course syllabus in your inbox

    Bivariate correlation

    T-test and non-parametric equivalents

    Chi-squared test

Get full course syllabus in your inbox

    Understanding formulas

    Linear and logistic regression models

    Regression plots

    Confounding / interaction in regression

    Evaluating residuals

    Scoring new data from models (prediction)

    Useful plots from regression models

Get full course syllabus in your inbox

    Tips to clear an Interview

    Common Interview questions and answers

    R programming Interview Questions and Answers

    Resume Building Guide

    Attempt for the related Global Certification Exam

    Earn Credentials and Start applying for Jobs

Get full course syllabus in your inbox

+ More Lessons

Need Customized curriculum?

Mock Interviews

Prepare & Practice for real-life job interviews by joining the Mock Interviews drive at Croma Campus and learn to perform with confidence with our expert team.Not sure of Interview environments? Don’t worry, our team will familiarize you and help you in giving your best shot even under heavy pressures.Our Mock Interviews are conducted by trailblazing industry-experts having years of experience and they will surely help you to improve your chances of getting hired in real.
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FAQ's

The R Programming Courses at Croma Campus shall be opted by any freshers who want to learn the language.

The R Programming Training does not ask for any requisites.

We accept all major kinds of payment options such as Net Banking, Cash, Card, etc.

You will receive globally recognized course completion certification from Croma Campus.

Yes, we provide interview tips that will help you to ace the examination.

Career Assistancecareer assistance
  • - Build an Impressive Resume
  • - Get Tips from Trainer to Clear Interviews
  • - Attend Mock-Up Interviews with Experts
  • - Get Interviews & Get Hired
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