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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.
    • Hostel facilities available at Rs.4,500/- per month for R Programming training in Gurgaon.
    • 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

four-star

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



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