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Best R Programming Training in Gurgaon

APTRON Gurgaon delivers the best R Programming training in Gurgaon to the students. Our experienced industry professionals just not train the students; rather, they impart the skill formula of value creation and addition during the employment tenure. An idea needs fluffiness; therefore, the R Programming course in Gurgaon covers both the basic and advanced training that enables students to understand the idea of R Programming in the latest technological implementations. Such training composes our students as the most preferred candidate for potential employers. Our R Programming training course syllabus has the expertise and capabilities to deliver quantifiable R Programming solutions to our students. APTRON creates skilled industry leaders through its unique training methodology. We create R Programming experts who help improvise capabilities of businesses by defying the latest threats, and challenges coming up in the domain.

APTRON is a well-established R Programming training institute in Gurgaon, which has acquired its reputation by imparting skill-based R Programming training to its students. Our R Programming course is delivered in accordance with the industry standards by industry-experienced professionals. In addition, our industrial training assists students to manage and execute real-time projects efficiently and competently in a professional environment.

Our expert industrial instructors are the core strength of APTRON. They possess advanced knowledge of R Programming concepts and principles. Trainers discuss, design and develop R Programming training methodologies based-on real-world business scenarios that have the ability to meet the latest industry requirements satisfactorily. Moreover, the architectural expertise in R Programming and the effective and clear translation of complex technologies is the excellent talents of our R Programming trainers. Also, the apprehensive approach of R Programming trainers assists students in understanding the theoretical concepts, practical implications and the importance of R Programming technology in the upcoming IT industry. Students with technical queries can get in touch with our trainers at any time of the day. The R Programming queries of the students are addressed patiently and repeatedly.

Students keen to learn R Programming can opt for R Programming training classes in Gurgaon on weekdays or weekends depending upon their requirements. APTRON, R Programming training institute in Gurgaon offers a fast-track R Programming training course and one-to-one training in Gurgaon to all the students.

APTRON’s R Programming training in Gurgaon is conducted both during weekdays as well as at the weekends depending upon the choice of the students. Also the institute offers fast-track R Programming training as well as one-to-one training in Gurgaon to all the students. The major topics covered under R Programming training in Gurgaon at APTRON are: Introduction to R Programming, Statistical Programming, Programming statistical graphics, Simulation, Computational linear algebra, Numerical optimization, Data Manipulation Techniques using R programming, R and Databases, Subscripting, Data Aggregation, Statistical Applications using R programming.

R Programming Training Batches

Being well aware of the industry needs, APTRON Gurgaon offers the R Programming Training in Gurgaon with major focus being on the practical aspects of the training. Our team of highly proficient R Programming trainers offer R Programming training in classroom and also we provide R Programming corporate training services. The R Programming training course syllabus here has been framed in a way to meet the professional requirements of both the beginner as well as the advanced level.

APTRON R Programming Training in Gurgaon has been categorised into following batches and is conducted in multiple batche during morning as well as evening.

Track	Fees	Regular	Weekend	Fast Track
Course Duration	Course Fees	45-60 Days	8 Weekend	5 Days
Hours	Course Fees	2 Hours a day	3 Hours a day	6+ Hours a day
Training Mode	Course Fees	Live Class Room	Live Class Room	Live Class Room

Key Features of R Programming Training at APTRON

Skill-based training Our instructors are industry specialized and certified academic entities. They bring in-depth and skill training that convenes the solutions to trending issues in the IT domain.

Limitless lab sessions APTRON has a credited and well-equipped IT establishment. It comprises smart classrooms and smart labs loaded with switches, data servers, servers, routers, Wi-Fi, live racks and other networking related devices.

100% placement assistance Our recruitment squad guarantees to fix maximum interview dates of R Programming applicants on a regular basis. The recruitment HR team carries out mock-interview sessions, shares often asked questions of interviews, opts for personality development sessions, Spoken English, and advances presentation skills of the students. All such classes help in structuring the confidence of the applicants as well as make his personality appealing for the interviews.

Reasonable fee APTRON accuse a reasonable fee and offers free of cost classes such as personality development, Spoken English, staging skills, performs interviews, and study materials. In addition, the timings are prepared flexible in accordance with students’ comfort.

Availability of free Demo classes
Industry expert trainers
Affordable course fee structure

30+ hours of Course Duration
Guidance for the certification exams
100% Placement assistance

R Programming Training Overview

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 4: 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
4 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

Top 10 Reasons to Choose APTRON for R Programming Training in Gurgaon

Students get the well-defined R Programming training in Gurgaon, and after completion of the R Programming course, they are placed in recognized organizations.
Our certified trainers having 5 to 15 years of industrial experience facilitate students with a regular, weekend or customized R Programming training in Gurgaon in accordance with their career requirements.
Trainers mentor their students in major project training, minor project training, live project preparation, and interview preparation.
Our established R Programming training institute has smart classrooms and smart labs equipped with the latest servers, routers, switches, nodes, projectors, live racks, Wi-Fi connectivity, digital pads, and other networking devices.
Students are free to access the labs for an unlimited number of hours as per their own preferred timings. In addition, students can avail Extra Time Slots (E.T.S.) for practical, absolutely free.
APTRON provides free Study Material, Books, PDFs, Video Trainings, Video Lectures, Sample Questions, Exam Preparation, Interview Questions, HR screening questions, Lab Guides and Projects.
There is no extra fee charged from the students who need to retake any R Programming class or require One-on-One attention from the instructors.
During the R Programming training, we also facilitate students with free of cost personality development sessions. These sessions comprise of spoken English, group discussions, mock interviews, presentation skills.
Our R Programming certification is globally recognized. We accept Cheque, Cash, Credit Card, Debit card, Net Banking payment modes.

R Programming Training in Gurgaon FAQ’s

What is the objective of APTRON Gurgaon R Programming Training in Gurgaon?

The purpose of R Programming training in Gurgaon at APTRON is:

The R Programming course is conveyed in adherence to the present industry requirements.
R Programming training pass on in-depth facts about the enterprise resource planning and centralized management.

R Programming Training Course Reviews

Best R Programming Training in Gurgaon

Reviewed by : Kajal

2 April 2018

5/5

I thank APTRON Solution for supporting me an elite R Programming industrial training that helped me attain the better place in my department at the office. The hands-on skill and the instructor’s leadership assisted me to study the language in short period of time. APTRON has a designed approach while coaching the R Programming training to the learners. The mentors have thorough knowledge about the R Programming insights and its specifications.

Student Testimonials

4.8/5(5698 Votes)

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R Programming Training Institute in Gurgaon - R Programming Course in Gurgaon

Best R Programming Training in Gurgaon

R Programming Training Batches

Key Features of R Programming Training at APTRON

R Programming Training Overview

Module 1: Essential to R programming

Module 2: Data Manipulation Techniques using R programming

Module 4: Statistical Applications using R programming

Top 10 Reasons to Choose APTRON for R Programming Training in Gurgaon

R Programming Training in Gurgaon FAQ’s

R Programming Training Course Reviews

Student Testimonials

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