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 Certification 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 Certification Course Outline

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 :

Basic Computer Knowledge: Familiarity with operating systems and file handling.
Mathematical Foundation: Understanding of statistics, probability, and algebra.
Logical Thinking: Ability to analyze problems and implement solutions.
Basic Programming Concepts: Prior experience with any programming language is helpful but not mandatory.
Data Analysis Interest: Enthusiasm for working with data, numbers, and patterns.
Understanding of Spreadsheets: Basic knowledge of Excel or Google Sheets for data handling.
No prior experience in R Programming is required, but having these basics will help in grasping concepts faster.

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”.