Topmost R Programming Training Course In Noida, Gurgaon, Delhi NCR

Courses Info

CTC is a leading institute which offers R Programming Training Course In Delhi NCR, Gurgaon, Noida. CTC is a quality oriented enterprise in IT industry and software development. R programming training institute offers many administrations. R Programming is a powerful statistical programming dialect which is utilized for prescient modelling and other data mining related techniques. R programming can be utilized for data aggregation Creating charts and plots, data manipulation, statistical Modelling. R programming is turning into the most sought after ability in the field of investigation for its open source credibility.

There are numerous spectacular packages accessible in R that will help in a concise data analysis. There is a colossal lack in the market for experts with skills in R programming which makes it all the more fascinating to seek after. The R Programming Training Course In Gurgaon, Delhi NCR, Noida offered a wide assortment of Training Courses in corporate and Industrial Training. Since R is a free programming it is being generally utilized which makes a sort of chances for proficient who are hoping to seek after a profession in R Programming.

What we do at R Programming Training Course In Delhi NCR, Gurgaon, Noida for R Programming?
Keeping in mind the end goal to become a successful expert in the field of analytics real time application ought to be examined in detail. Hands on Experience with the blend of statistical concept will be given by just specialists who are managing genuine situations in R programming consistently in their respective industry.

We are here to trained you in R Programming or R Analytics. We have professional experts working in MNCs and have more than 10 years experience in the analytics field.

  • All the training would be provided by Industry Experts who already works on R Analytics
  • Backup Class in case you miss any session.
  • Theory + Practical Training along with case studies in order to get better understanding of concepts.
  • Complete course material with no extra cost.
  • Free doubt clearing session after completion of the training.
  • Resume building by experts.
  • Feedback form filled by candidates after every class in order to maintain highest level of quality standards.
    • Case Study Implementation and Assignments

    CTC-R Programming

     

    Who this course is for:

    • This course is for you if you want to learn how to program in R
    • No prior knowledge or experience needed. Only a passion to be successful!
    • Trained by working Industry professionals.
    • Anybody who has no or basic R knowledge and would like to take their skills to the next level
    • This course is for you if you look for Assignments and real time industry project exposure in R

    Outline: In this course you will learn:

    • Overview
    • Environment Setup
    • How to prepare data for analysis in R
    • Learn how to use R Studio
    • Learn the core principles of programming
    • Learn how to create vectors in R
    • Learn how to create variables
    • Learn about integer, double, logical, character and other types in R
    • How to perform the median imputation method in R
    • How to work with date-times in R
    • What Lists are and how to use them
    • What the Apply family of functions is
    • How to use apply(), lapply() and sapply() instead of loops
    • How to nest your own functions within apply-type functions
    • How to nest apply(), lapply() and sapply() functions within each other
    • Learn how to create a while() loop and a for() loop in R
    • Learn how to build and use matrices in R
    • Learn the matrix() function, learn rbind() and cbind()
    • Learn how to install packages in R
    • Introduction and Overview to R
    • Environment Setup (Installation and setting up R IDE setup.)
    • R Basics
    • Basic Syntax
    • Data Types
    • Variables
    • Operators
    • Decision Making
    •  
    • Loops
    • Functions
    • Strings
    • Vectors
    • Lists
    • Matrices
    • Arrays
    • Factors
    •  
    • Data Frames
    • Packages
    • Data Reshaping
    • R Data Interfaces
    • CSV Files
    • Excel Files
    • Binary Files
    • XML Files
    •  
    • JSON Files
    • Web Data
    • Database
    • R Charts & Graphs
    • Pie Charts
    • Bar Charts
    • Boxplots
    • Histograms
    •  
    • Line Graphs
    • Scatterplots
    • R Statistics Examples (Advanced)
    • Mean, Median & Mode
    • Linear Regression
    • Multiple Regression
    • Logistic Regression
    • Normal Distribution
    •  
    • Binomial Distribution
    • Poisson Regression
    • Analysis of Covariance
    • Time Series Analysis
    • Nonlinear Least Square
    • Decision Tree
    • Random Forest
    • Survival Analysis
    • Chi Square Tests
    • Assignment and Case studies
    •  
    • Case Studies: Implementation of the concepts in real time Industry based case studies.
  • Defining the R project
  • Obtaining R
  • Using the R console
  • A sample R session
  • Basic programming concepts
  • Scripts
  • Text editors for R
  • Graphical User Interfaces (GUIs) for R
  • Packages
  • Variable classes (factor, numeric, logical, complex, missing)
  • Vectors and matrices
  • Data frames and lists
  • Data sets included in R packages
  • Summarizing and exploring data
  • Reading data from external files
  • Storing data to external files
  • Creating and storing R workspaces
  • Basic exploratory graphics
  • Mathematical operations
  • Basic matrix computation
  • Textual operations
  • Searches, strings, and pattern matching
  • Regular sequences
  • Random sequences
  • Sampling from distributions
  • More slicing and extracting data
  • Basic plots
  • Adding overlaid lines, text, etc.
  • Graphical parameters
  • Data exploration
  •  t-tests
  •  ANOVA
  •  Sorting/rearranging data structures
  • General modeling syntax
  • Extracting model results
  •  Confidence intervals
  •  Graphics for regression
  •  Tabular displays
  •  Extracting model results
  •  Confidence intervals
  •  Regression diagnostics
  •  3D graphics
  •  Graphics presentation
  •  Interactive graphics
  • Animations
  •  High-density data displays
  •  Heatmaps
  •  Partitioning graphics
  •  Applying functions
  •  Writing your own functions
  •  Modifying existing functions
  •  Permutation testing
  •  Bootstrapping
  •  Cross-validation methods