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Course Name Duration Classes Total Fee Mode of Training Class Timing
R Programming Language
6 Months
120
60,000 PKR
Online & Face-to-Face
To be decided mutually with students
R Programming Language

Programming In R Language Course

Programming in r Language  course Start your career as a  Professional R Programming Developer.

Over the past few years, R has been steadily gaining popularity with business analysts, statisticians and data scientists as a tool of choice for conducting statistical analysis of data as well as supervised and unsupervised machine learning.

Get Job Ready with Highly-Demanded Skill for Analytics. Learn and Master from the Best IT Training Institute of Rawalpindi.

R is an open-source, a popular programming language that offers robust statistical techniques and concise data visualization skills.

In a few decades, its popularity has increased drastically worldwide.

For us, our Students are our top priority. The training program and curriculum have also been designed in such a smart way that the students can get familiar with it in Technocation in Rawalpindi. They also provide industrial professionalism since the commencement of the training and until the fulfillment of the particular curriculum.

Therefore, we aim to shape inspiring students with in-depth training to meet the requirements of the IT industry and build substantial grounds in R Development Training by exhibiting students with various projects. Technocation also bestows the Best R Training Course in Rawalpindi, Islamabad.

We guide people from every background to change their lives via our career-oriented short term courses in Rawalpindi. Our evening and online course primarily focus on college students and full/part-time employees.

For Furthermore detail you can see the outline.

R programming Course content

Session 1: WHAT IS R

  • What is R?
  • Positioning of R in the Data Science Space
  • The Legal Aspects
  • Microsoft R Open
  • R Integrated Development Environments
  • Running R
  • Running RStudio
  • Getting Help
  • General Notes on R Commands and Statements
  • Assignment Operators
  • R Core Data Structures
  • Assignment Example

Session 2:WHAT IS R

  • System Date and Time
  • R Objects and Workspace
  • Printing Objects
  • Arithmetic Operators
  • Logical Operators
  • Operations
  • User-defined Functions
  • Control Statements
  • Conditional Execution
  • Repetitive Execution
  • Repetitive execution
  • Built-in Functions
  • Summary

Session 3:INTRODUCTION TO FUNCTIONAL PROGRAMMING WITH R

  • What is Functional Programming (FP)?
  • Terminology: Higher-Order Functions
  • A Short List of Languages that Support FP
  • Functional Programming in R
  • Vector and Matrix Arithmetic
  • Vector Arithmetic Example
  • More Examples of FP in R
  • Summary

Session 4:MANAGING YOUR ENVIRONMENT

  • Getting and Setting the Working Directory
  • Getting the List of Files in a Directory
  • The R Home Directory
  • Executing External R commands
  • Loading External Scripts in RStudio
  • Listing Objects in Workspace
  • Removing Objects in Workspace
  • Saving Your Workspace in R
  • Saving Your Workspace in RStudio
  • Saving Your Workspace in R GUI
  • Loading Your Workspace
  • Diverting Output to a File
  • Batch (Unattended) Processing
  • Controlling Global Options
  • Summary

Session 5:R TYPE SYSTEM AND STRUCTURES

  • The R Data Types
  • System Date and Time
  • Formatting Date and Time
  • Using the mode() Function
  • R Data Structures
  • What is the Type of My Data Structure?
  • Creating Vectors
  • Logical Vectors
  • Character Vectors
  • Factorization
  • Multi-Mode Vectors
  • The Length of the Vector
  • Getting Vector Elements
  • Lists

Session 6:R TYPE SYSTEM AND STRUCTURES

  • A List with Element Names
  • Extracting List Elements
  • Adding to a List
  • Matrix Data Structure
  • Creating Matrices
  • Creating Matrices with cbind() and rbind()
  • Working with Data Frames
  • Matrices vs Data Frames
  • A Data Frame Sample
  • Creating a Data Frame
  • Accessing Data Cells
  • Getting Info About a Data Frame
  • Selecting Columns in Data Frames
  • Selecting Rows in Data Frames

Session 7:Projects CSS3

  • Getting a Subset of a Data Frame
  • Sorting (ordering) Data in Data Frames by Attribute(s)
  • Editing Data Frames
  • The str() Function
  • Type Conversion (Coercion)
  • The summary() Function
  • Checking an Object’s Type
  • Summary

Session 8:EXTENDING R

  • The Base R Packages
  • Loading Packages
  • What is the Difference between Package and Library?
  • Extending R
  • The CRAN Web Site
  • Extending R in R GUI
  • Extending R in RStudio
  • Installing and Removing Packages from Command-Line
  • Summary
  •  

Session 9:READ-WRITE AND IMPORT-EXPORT OPERATIONS IN R

  • Reading Data from a File into a Vector
  • Example of Reading Data from a File into A Vector
  • Writing Data to a File
  • Example of Writing Data to a File
  • Reading Data into A Data Frame
  • Writing CSV Files
  • Importing Data into R
  • Exporting Data from R
  • Summary

Session 10:STATISTICAL COMPUTING FEATURES IN R

  • Statistical Computing Features
  • Descriptive Statistics
  • Basic Statistical Functions
  • Examples of Using Basic Statistical Functions
  • Non-uniformity of a Probability Distribution
  • Writing Your Own skew and kurtosis Functions
  • Generating Normally Distributed Random Numbers
  • Generating Uniformly Distributed Random Numbers
  • Using the summary() Function
  • Math Functions Used in Data Analysis
  • Examples of Using Math Functions
  • Correlations
  • Correlation Example
  •  

Session 11:STATISTICAL COMPUTING FEATURES IN R

  • Testing Correlation Coefficient for Significance
  • The cor.test() Function
  • The cor.test() Example
  • Regression Analysis
  • Types of Regression
  • Simple Linear Regression Model
  • Least-Squares Method (LSM)
  • LSM Assumptions
  • Fitting Linear Regression Models in R
  • Example of Using lm()
  • Confidence Intervals for Model Parameters
  • Example of Using lm() with a Data Frame
  • Regression Models in Excel
  • Multiple Regression Analysis
  • Summary

Session 12:DATA MANIPULATION AND TRANSFORMATION IN R

  • Applying Functions to Matrices and Data Frames
  • The apply() Function
  • Using apply()
  • Using apply() with a User-Defined Function
  • apply() Variants
  • Using tapply()
  • Adding a Column to a Data Frame
  • Dropping A Column in a Data Frame
  • The attach() and detach() Functions
  • Sampling
  • Using sample() for Generating Labels
  • Set Operations

Session 14:DATA MANIPULATION AND TRANSFORMATION IN R

  • Example of Using Set Operations
  • The dplyr Package
  • Object Masking (Shadowing) Considerations
  • Getting More Information on dplyr in RStudio
  • The search() or searchpaths() Functions
  • Handling Large Data Sets in R with the data.table Package
  • The fread() and fwrite() functions from the data.table Package
  • Using the Data Table Structure
  • Summary

Session 14:DATA VISUALIZATION IN R

  • Data Visualization
  • Data Visualization in R
  • The ggplot2 Data Visualization Package
  • Creating Bar Plots in R
  • Creating Horizontal Bar Plots
  • Using barplot() with Matrices
  • Using barplot() with Matrices Example
  • Customizing Plots
  • Histograms in R
  • Building Histograms with hist()
  • Example of using hist()

Session 14:DATA VISUALIZATION IN R

  • Pie Charts in R
  • Examples of using pie()
  • Generic X-Y Plotting
  • Examples of the plot() function
  • Dot Plots in R
  • Saving Your Work
  • Supported Export Options
  • Plots in RStudio
  • Saving a Plot as an Image
  • Summary

Session 16:USING R EFFICIENTLY

  • Object Memory Allocation Considerations
  • Garbage Collection
  • Finding Out About Loaded Packages
  • Using the conflicts() Function
  • Getting Information About the Object Source Package with the pryr Package
  • Using the where() Function from the pryr Package
  • Timing Your Code
  • Timing Your Code with system.time()
  • Timing Your Code with System.time()
  • Sleeping a Program
  • Handling Large Data Sets in R with the data.table Package
  • Passing System-Level Parameters to R
  • Summary

Session 17:LAB EXERCISES

  • Lab 1 – Getting Started with R
  • Lab 2 – Learning the R Type System and Structures
  • Lab 3 – Read and Write Operations in R
  • Lab 4 – Data Import and Export in R
  • Lab 5 – k-Nearest Neighbors Algorithm
  • Lab 6 – Creating Your Own Statistical Functions
  • Lab 7 – Simple Linear Regression
  • Lab 8 – Monte-Carlo Simulation (Method)
  • Lab 9 – Data Processing with R
  • Lab 10 – Using R Graphics Package
  • Lab 11 – Using R Efficiently

Session 18:Additional Topics Included in this project.

  • How to start freelancing?
  • How to start our business?
  • How to create CV for job?
  • Reference for job
  • Totally practical this course

  • Experience and Inspiring Trainers:

    Our trainers bring their years of industry experience during the course. They are expert and passionate about delivering inspiring training as they know training inside out. They will advise you on all the options to make sure you get the best possible result.
     

  • Real-time Practice and Projects :

    Our R courses are comprehensive and practical. Moreover we work on industry related projects. If you have a project or an idea that you wanted to turn into project then present that idea after completion of course and our trainers especially will help you to work on your own project which helps you to become confident and satisfied.
     

  • Prerequisites for R Course :

     You should have basic C,C++ knowledge.
     

  • Certificate :

    Finally completing this training you will receive a course completion certificate along with internship in R Training so you can get recognition for your new skills.
     

  • Course Material :

     Softy Copy notes are briefly included in this course
     

  • Support and Careers Advice :

    In the end our trainers are always ready to help you for any problems or question regarding R. We prepare students for facing Interview questions on R and help them to build their online resume. Our more than 80% students are placed in good MNCs.

    “Our Student Success is Our Mission”​.