R Programming Training

4 Star Rating: Very Good 4.50 out of 5 based on 273 ratings.

R Programming is a statistical programming language deployed for predictive modelling and data mining techniques. In addition to this, it is also used for data aggregation, charts and plots creation, data manipulation and statistical modelling. Owing to its open source credibility it is one of the most sought after skill in the vertical of analytic programming.

Due to the shortage of personals having the knowledge of this language, there is a huge demand for R programmers. Therefore, R programming is one of the languages that top the list of the students who want to have a bright future. If you are looking for a training institute which offers R Programming training institute in Noida, then Croma Campus is the right destination for you. It is one of the leading institutes offering complete and real time training of R Programming in Noida.

For becoming a successful R Programmer, it is mandatory to study the language in detail. At Croma Campus, we train our students by following a combination of statistical concepts and hands-on practical training. At our R Programming Coaching Centre, we provide training to our students with world-class faculty holding an experience of several years. Since our entire faculty is working in reputed organizations, they are fully aware of the current trends of R Programming in Noida.

At Croma Campus, the most experienced, R Programming Training Institute in Noida, we split the entire syllabus into different modules, making it easy for our students to grasp the R Programming concepts.

• Introduction To R Programming
• Learning R Data Structure
• Import Data
• Manipulation Of Data
• Usage Of Functions In R
• R Programs
• Plots & Charts

At our R Programming Coaching Centre, we offer guaranteed placement to all our students in leading organizations and MNCs. Our clients include some of the reputed organizations and hence our students are offered with bright future ahead after being trained from our R Programming in Noida centre. We conduct Regular, Weekend training and Fast Track training batches for facilitating our students to balance between their colleges and training sessions.

Key Features of R Programming Training are:

  • Design POC (Proof of Concept): This process is used to ensure the feasibility of the client application.
  • Video Recording of every session will be provided to candidates.
  • Live Project Based Training.
  • Job-Oriented Course Curriculum.
  • Course Curriculum is approved by Hiring Professionals of our client.
  • Post Training Support will helps the associate to implement the knowledge on client Projects.
  • Certification Based Training are designed by Certified Professionals from the relevant industries focusing on the needs of the market & certification requirement.
  • Interview calls till placement.

Module 1: Essential to R programming

An Introduction to R

  • History of S and R
  • Introduction to R
  • The R environment
  • What is Statistical Programming?
  • Why use a command line?
  • Your first R session

Introduction to the R language

  • Starting and quitting R
  • Recording your work
  • Basic features of R
  • Calculating with R
  • Named storage
  • Functions
  • Exact or approximate?
  • R is case-sensitive
  • Listing the objects in the workspace
  • Vectors
  • Extracting elements from vectors
  • Vector arithmetic
  • Simple patterned vectors
  • Missing values and other special values
  • Character vectors
  • Factors
  • More on extracting elements from vectors
  • Matrices and arrays
  • Data frames
  • Dates and times
  • Built-in functions and online help
  • Built-in examples
  • Finding help when you don’t know the function name
  • Built-in graphics functions
  • Additional elementary built-in functions
  • Logical vectors and relational operators
  • Boolean algebra
  • Logical operations in R
  • Relational operators
  • Data input and output
  • Changing directories
  • dump() and source()
  • Redirecting R output
  • Saving and retrieving image files
  • Data frames and the read.table function

Programming statistical graphics

  • High-level plots
  • Bar charts and dot charts
  • Pie charts
  • Histograms
  • Box plots
  • Scatterplots
  • QQ plots
  • Choosing a high-level graphic
  • Low-level graphics functions
  • The plotting region and margins
  • Adding to plots
  • Setting graphical parameters

Programming with R

  • Flow control
  • The for() loop
  • The if() statement
  • The while() loop
  • Newton’s method for root finding
  • The repeat loop, and the break and next statements
  • Managing complexity through functions
  • What are functions?
  • Scope of variables
  • Miscellaneous programming tips
  • Using fix()
  • Documentation using#
  • Some general programming guidelines
  • Top-down design
  • Debugging and maintenance
  • Recognizing that a bug exists
  • Make the bug reproducible
  • Identify the cause of the bug
  • Fixing errors and testing
  • Look for similar errors elsewhere
  • The browser() and debug()functions
  • Efficient programming
  • Learn your tools
  • Use efficient algorithms
  • Measure the time your program takes
  • Be willing to use different tools
  • Optimize with  care

Simulation

  • Monte Carlo simulation
  • Generation of pseudorandom numbers
  • Simulation of other random variables
  • Bernoulli random variables
  • Binomial random variables
  • Poisson random variables
  • Exponential random numbers
  • Normal random variables
  • Monte Carlo integration
  • Advanced simulation methods
  • Rejection sampling
  • Importance sampling

Computational linear algebra

  • Vectors and matrices in R
  • Constructing matrix objects
  • Accessing matrix elements; row and column names
  • Matrix properties
  • Triangular matrices
  • Matrix arithmetic
  • Matrix multiplication and inversion
  • Matrix inversion
  • The LU decomposition
  • Matrix inversion in R
  • Solving linear systems
  • Eigenvalues and eigenvectors
  • Advanced topics
  • The singular value decomposition of a matrix
  • The Choleski decomposition of a positive definite matrix
  • The QR decomposition of a matrix
  • The condition number of a matrix
  • Outer products
  • Kronecker products
  • apply()

Numerical optimization

  • The golden section search method
  • Newton–Raphson
  • The Nelder–Mead simplex method
  • Built-in functions
  • Linear programming
  • Solving linear programming problems in R
  • Maximization and other kinds of constraints
  • Special situations
  • Unrestricted variables
  • Integer programming
  • Alternatives to lp()
  • Quadratic programming

Module 2: Data Manipulation Techniques using R programming

Data in R

  • Modes and Classes
  • Data Storage in R
  • Testing for Modes and Classes
  • Structure of R Objects
  • Conversion of Objects
  • Missing Values
  • Working with Missing Values

Reading and Writing Data

  • Reading Vectors and Matrices
  • Data Frames: read.table
  • Comma- and Tab-Delimited Input Files
  • Fixed-Width Input Files
  • Extracting Data from R Objects
  • Connections
  • Reading Large Data Files
  • Generating Data
  • Sequences
  • Random Numbers
  • Permutations
  • Random Permutations
  • Enumerating All Permutations
  • Working with Sequences
  • Spreadsheets
  • The RODBC Package on Windows
  • The gdata Package (All Platforms)
  • Saving and Loading R Data Objects
  • Working with Binary Files
  • Writing R Objects to Files in ASCII Format
  • The write Function
  • The write.table function
  • Reading Data from Other Programs

R and Databases

  • A Brief Guide to SQL
  • Navigation Commands
  • Basics of SQL
  • Aggregation
  • Joining Two Databases
  • Subqueries
  • Modifying Database Records
  •  ODBC
  • Using the RODBC Package
  • The DBI Package
  • Accessing a MySQL Database
  • Performing Queries
  • Normalized Tables
  • Getting Data into MySQL
  • More Complex Aggregations

Dates

  • Date
  • The chron Package
  • POSIX Classes
  • Working with Dates
  • Time Intervals
  • Time Sequences

Factors

  • Using Factors
  • Numeric Factors
  • Manipulating Factors
  • Creating Factors from Continuous Variables
  • Factors Based on Dates and Times
  • Interactions

Subscripting

  • Basics of Subscripting
  • Numeric Subscripts
  • Character Subscripts
  • Logical Subscripts
  • Subscripting Matrices and Arrays
  • Specialized Functions for Matrices
  • Lists
  • Subscripting Data Frames

Character Manipulation

  • Basics of Character Data
  • Displaying and Concatenating Character
  • Working with Parts of Character Values
  • Regular Expressions in R
  • Basics of Regular Expressions
  • Breaking Apart Character Values
  • Using Regular Expressions in R
  • Substitutions and Tagging

Data Aggregation

  • Table
  • Road Map for Aggregation
  • Mapping a Function to a Vector or List
  • Mapping a function to a matrix or array
  • Mapping a Function Based on Groups
  • There shape Package
  • Loops in R

Reshaping Data

  • Modifying Data Frame Variables
  • Recoding Variables
  • The recode Function
  • Reshaping Data Frames
  • The reshape Package
  • Combining Data Frames
  • Under the Hood of merge

Module 3: Statistical Applications using R programming

Basics

  • First steps 
  • An overgrown calculator
  • Assignments
  • Vectorized arithmetic
  • Procedures
  • Graphics
  • R language essentials
  • Expressions and objects
  • Functions and arguments
  • Vectors
  • Quoting and escape sequences
  • Missing values
  • Functions that create vectors
  • Matrices and arrays
  • Factors
  • Lists
  • Data frames
  • Indexing
  • Conditional selection
  • Indexing of data frames
  • Grouped data and data frames
  • Implicit loops
  • Sorting

The R Environment

  • Session management
  • The workspace
  • Textual output
  • 3 Scripting
  • Getting help
  • Packages
  • Built-in data
  • attach and detach
  • subset, transform, and within
  • The graphics subsystem
  • Plot layout
  • Building a plot from pieces
  • Using par
  • Combining plots
  • R programming
  • Flow control
  • Classes and generic functions
  • Data entry
  • Reading from a text file
  • Further details on read.table
  • The data editor
  • Interfacing to other programs

Probability and distributions

  • Random sampling
  • Probability calculations and combinatorics
  • Discrete distributions
  • Continuous distributions
  • The built-in distributions in R
  • Densities
  • Cumulative distribution functions
  • Quantiles
  • Random numbers

Descriptive statistics and graphics

  • Summary statistics for a single group
  • Graphical display of distributions
  • Histograms
  • Empirical cumulative distribution
  • Q–Q plots
  • Boxplots
  • Summary statistics by groups
  • Graphics for grouped data
  • Histograms
  • Parallel boxplots
  • Stripcharts
  • Tables
  • Generating tables
  • Marginal tables and relative frequency
  • Graphical display of tables
  • Barplots
  • Dotcharts
  • Piecharts

One- and two-sample tests

  • One-sample t test
  • Wilcoxon signed-rank test
  • Two-sample t test
  • Comparison of variances
  • Two-sample Wilcoxon test
  • The paired t test
  • The matched-pairs Wilcoxon test

Regression and correlation

  • Simple linear regression
  • Residuals and fitted values
  • Prediction and confidence bands
  • Correlation
  • Pearson correlation
  • Spearman’s ?
  • Kendall’s ?

Analysis of variance and the Kruskal–Wallis test

  • One-way analysis of variance
  • Pairwise comparisons and multiple testing
  • Relaxing the variance assumption
  • Graphical presentation
  • Bartlett’s test
  • Kruskal–Wallis test
  • Two-way analysis of variance
  • Graphics for repeated measurements
  • The Friedman test
  • The ANOVA table in regression analysis

Tabular data

  • Single proportions
  • Two independent proportions
  • k proportions, test for trend
  • r × c tables

Power and the computation of sample size

  • The principles of power calculations
  • Power of one-sample and paired t tests
  • Power of two-sample t test
  • Approximate methods
  • Power of comparisons of proportions
  • Two-sample problems
  • One-sample problems and paired tests
  • Comparison of proportions

Advanced data handling

  • Recoding variables 
  • The cut function
  • Manipulating factor levels
  • Working with dates
  • Recoding multiple variables
  • Conditional calculations
  • Combining and restructuring data frames
  • Appending frames
  • Merging data frames
  • Reshaping data frames
  • Per-group and per-case procedures
  • Time splitting

Multiple Regression

  • Plotting multivariate data
  • Model specification and output
  • Model search

Linear models

  • Polynomial regression
  • Regression through the origin
  • Design matrices and dummy variables
  • Linearity over groups
  • Interactions
  • Two-way ANOVA with replication
  • Analysis of covariance
  • Graphical description
  • Comparison of regression lines
  • Diagnostics

Logistic regression

  • Generalized linear models
  • Logistic regression on tabular data
  • The analysis of deviance table
  • Connection to test for trend
  • Likelihood profiling
  • Presentation as odds-ratio estimates
  • Logistic regression using raw data
  • Prediction
  • Model checking

Survival analysis

  • Essential concepts
  • Survival objects
  • Kaplan–Meier estimates
  • The log-rank test
  • The Cox proportional hazards model

Rates and Poisson regression

  • Basic ideas
  • The Poisson distribution
  • Survival analysis with constant hazard
  • Fitting Poisson models
  • Computing rates
  • Models with piecewise constant intensities

Nonlinear curve fitting

  • Basic usage
  • Finding starting values
  • Self-starting models
  • Profiling
  • Finer control of the fitting algorithm

Please write to us at info@cromacampus.com for the course price, schedule & location.

Enquire Now

Frequently Asked Questions:

All training courses offered by us are through IT Professional with 10+ years of experience. Freshers/College Students/Professionals(IT & Non-IT) can spot the quality of training by attending one lecture. Hence, we provide one free demo class to all our trainees so that they can judge on their own.

No, you don’t have to pay anything to attend the demo class. You are required to pay the training fee after free demo only if you are fully satisfied and want to continue the training.

To register for free demo, visit our campus or call our counsellors on the numbers given on contact us page.

Yes, all the trainees shall work on live projects provided by Croma Campus after completing their training part.

You will never lose any lecture. You can choose either of the two options:
View the recorded session of the class available in your LMS.
You can attend the missed session, in any other live batch.

Please note, access to the course material will be available for lifetime once you have enrolled into the course.

Yes, Training certificate & Project completion will be issued by Croma Campus(ISO 9001-2000 Certified Training Center)

Yes, Croma Campus conduct special training programs on week end for college students throughout the year.

Croma Campus is the largest education company and lots of recruitment firms contacts us for our students profiles from time to time. Since there is a big demand for this skill, we help our certified students get connected to prospective employers. We also help our customers prepare their resumes, work on real life projects and provide assistance for interview preparation. Having said that, please understand that we don’t guarantee any placements however if you go through the course diligently and complete the project you will have a very good hands on experience to work on a Live project.

Yes, Course Fee can be paid in two equal installments with prior Approval.

Yes, Croma Campus offer various group or special discounts.

No, Lab is open from 8 A.M. to 8 P.M. seven days a week. This time can be extended upto 11 PM if need arises.

Yes, students can take breaks during their exams and can resume it later without paying any fee. Apart from this, Students can attend batches for revision even after completion of their courses.

Batch strength differ from technology to technology. Minimum batch strength at Croma Campus is 10 and maximum batch strength is 30.

Drop us a query

Course Features

Get Practical and Well focused training from Top IT Industry experts.

Get Routine assignments based on learning from previous classes.

Live project, during or after the completion of the syllabus.

Lifetime access to the learning management system including Class recordings, presentations, sample code and projects

Lifetime access to the support team (available 24/7) in resolving queries during and after the course completion

Get certification after the course completion.

+91-9711526942 whatsapp

Testimonials