Best R Programming Training in Delhi & Best R Programming Training Institute in Delhi

Best r-programming training institute in delhi
Best R Programming training in delhi 4 out of 5 based on 423 ratings. 5 user reviews.

Join Best R Programming Training in Delhi, R Programming Course in Delhi, R Programming Institute in Delhi

APTRON Delhi offers an inclusive R Programming training in delhi. The extensive practical training provided by R Programming training institute in delhi equips live projects and simulations. Such detailed R Programming course has helped our students secure job in various MNCs. The trainers at APTRON Delhi are subject specialist corporate professionals providing in-depth study in R Programming course in delhi. Participants completing the R Programming certification have plethora of job opportunities in the industry.

Further, we have kept the R Programming course in delhi duration flexible. From online classroom to fast-track & one-to-one classroom R Programming training is provided during weekdays and weekends to the attendees. Our modern lab is equipped with latest technologies helping students avail a successful R Programming training and certification from the institute.

APTRON Delhi, recognized among the top ten R Programming training institute in delhi, has training module for beginners, intermediates, and experts. Whether you are a college student, I.T professional or a project manager; the best R Programming training institute in delhi offers best training environment, veteran R Programming trainers, and flexible training schedules for entire modules. Also, the best training institute for R Programming training in delhi asks for a value to money fee from the students. The pocket-friendly R Programming course fee structure can be afford by students coming from all walks of life.

After R Programming course in delhi, learning the interview skills indeed becomes mandatory. Along with R Programming classes in delhi, we have sessions for personality development, spoken English, and presentation. At our R Programming training centre in delhi, Placement team schedules recruitment drives where the technology-driven branded companies hand-pick our students. R Programming training in delhi with placement assistance is the key feature which rated us 'star five' in the reviews by our aspirants. Reviews and honest feedback is mentioned on our official website. APTRON Delhi is one of the best R Programming training centres in delhi delivering out-of-box thinking professionals to the industry.

APTRON Delhi has a modern lab equipped with latest devices that facilitate participants in having a thorough hands-on experience through live projects. Such training in delhi boost the confidence level in participants to face the real-time challenges successfully in a job. The R Programming syllabus includes for R Programming course module on real time projects along with placement assistance. R Programming topics covered 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 & Many more. Check the duration, course content and syllabus given below.

R Programming Course Fee and Duration
Track Regular Track Weekend Track Fast Track
Course Duration 45 - 60 Days 8 Weekends 5 Days
Hours 2 hours a day 3 hours a day 6+ hours a day
Training Mode Live Classroom Live Classroom Live Classroom

Course Content Covered in R Programming Training Course

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

Top 20 Reasons to Choose APTRON for R Programming Training in Delhi

  • Our R Programming training in Delhi is developed in compliance to current IT industry.
  • We provide the best R Programming training in Delhi covering entire course modules during the R Programming classes. Also, students avail R Programming course in Delhi with placement assistance.
  • R Programming training in Delhi are scheduled on weekdays and weekends. Also students can opt for customized schedule according to the requirements.
  • Our team of trainers are industry-experts possessing more than a decade experience in training.
  • Mentors coaching R Programming training in Delhi not only help students in accomplishing live projects, but also provide session on interview preparation along with placement assistance.
  • Ultra-modern I.T laboratory equipped with latest infrastructure.
  • Our lab is open 365 days in a year. Students, according to their convenience can utilize the lab for completing projects and practice the technical assignments.
  • Our training classrooms are equipped with modern I.T infrastructure such as projectors, live racks, Wi-Fi, and digital pads.
  • We facilitate our students with glass-door study room and discussion zone area (meeting room) to enhance their learning and exploring abilities.
  • Along with technical training and course, we organize no cost sessions on personality development spoken English, group discussion, mock interview and presentation skills to develop high level of confidence in students.
  • We also organize no cost personality development and presentation seminars.
  • Our course material includes books, and soft copies of tutorials in the form of PDFs, sample papers, technical and HR interview questions, and projects available on our website.
  • Students enrolled to R Programming training in Delhi can also avail hostel facility at Rs.4,500/- a month.
  • We facilitate students with no cost study material, soft copies of PDFs, video training, sample questions for respective certification, and interview questions along with lab guides made available on our website for quick access.
  • Our certificates are globally recognized provided after completion of course.
  • We facilitate students with Extra Time Slots (E.T.S) for doing unlimited practical at no cost.
  • According to the requirements, students can retake the class at no cost.
  • Our instructors pay one-to-one attention.
  • To enhance knowledge of the students, the complex technical concepts are imparted through easy coaching.
  • We accept master and visa cards (Debit & Credit), also payment mode cash, cheque, and Net Banking available.

APTRON Delhi Trainer's Profile for R Programming Training in Delhi

APTRON's Delhi R Programming Trainers are:

  • Our trainers are industry-experts and subject specialists who have mastered on running applications providing best R Programming training to the students.
  • We have received various prestigious awards by our recognized IT partners and organizations.
  • Our trainers are MNC working professionals employed in HCL Technologies, Birla-soft, TCS, IBM, Sapient, Agilent Technologies, and so on.
  • Our trainers are certified professionals possessing 7+ years of experience in the industry.
  • Our trainers have regular coordination with MNCs HR team on daily basis.

Placement Assistance after R Programming Training in Delhi

Along with R Programming training in Delhi, we provide placement assistance to the students.

  • APTRON Delhi with successful 96% placement rate has a dedicated HR wing that assist students in securing placement according to their requirements.
  • APTRON Delhi assist students in developing their resume matching the current industry needs.
  • APTRON Delhi, apart from course training, also facilitate students with sessions provided on personality development, spoken English, group discussion, mock interview, and presentation skills to develop a high level of confidence for facing tricky and challenging interviews competently.
  • APTRON Delhi provide an in-depth training to the students, which assist them to secure placement in top IT firms such as HCL, TCS, Infosys, Wipro, Accenture, and many more effortlessly.

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R Programming Training in Delhi Reviews

R Programming Training in Delhi
Reviewed by
Rashida
on
APTRON training is remarkable training center in Delhi for R Programming with organized course-ware. I am quite sure that I will get R Programming profession job shortly.

Rating:
5/5 r programming training delhi
R Programming training in Delhi
Reviewed by
Atul Kumar
on
APTRON institute is brilliant training center for R Programming certification. You will get the impeccable R Programming certification training in Delhi.

Rating:
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R Programming Training Institute in Delhi
Reviewed by
Roshni Kumari
on
I got trained in R Programming certification from APTRON training institute in Delhi, got trained in course very well. Waiting for R Programming certification placement.

Rating:
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R Programming training in Delhi
Reviewed by
Sakshi Rawat
on
APTRON training center is suggested by my brother for the most excellent R Programming training in Delhi. I like the practical training classes for R Programming course.

Rating:
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R Programming Training Delhi
Reviewed by
Kamal Kumar
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APTRON training center in Delhi is recommend by one of my friend. I have finished R Programming training and now I am attending interviews.

Rating:
5/5 r programming training delhi
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