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Programming in R

Overview

  • Total Duration 44 Hours
  • Course Type Classroom + Online

Classroom + app based learning

Industry Experience Faculty

Faculty Guidance through app

35 mn learners worldwide

ENROLL NOW!

This course is not accepting online registrations at this time

  • Learn the most popular tool for data analytics
  • Start with the R basics, to advance Programming in R
  • Hybrid Learning with Guided practice & Weekly Practice quiz questions on the app along with the classroom sessions
  • Extensive Learning hours with 44 hours of classroom training including the classroom assessment with additional 42 hours of online guided practice for better learning and increased retention

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Program starts with basics of R Programming, evolving into How to work with Data in R; Importing the Data, Preparing the Data, Analysing the Data in R and Visualization of the results in R.

 

  Learning Objectives

1. Understanding of R System and installation and configuration of R-Environment and R-Studio

2. Understanding R Packages, their installation and management

3. Understanding of nuts and bolts of R:

               a. R program Structure

               b. R Data Type, Command Syntax and Control Structures

               c. File Operations in R

4. Application of R Programming in Daily life problems

5. Preparing Data in R

             a. Data Cleaning

             b. Data imputation

            c. Data conversion

6. Visualizing data using R with different type of graphs and charts

7. Applying R Advance features to solve complex problems and finetuning R Processes

  • Introduction to Programming in R
  • Data Preparation in R
  • Data Visualization in R
  • Advanced R Programming 
  • Have basic knowledge of working in the Windows environment and Microsoft excel
  • Knowledge of Maths/Statistics upto Class XII

FAQs

Who should join this course?

Undergraduates and graduates looking to enter the domain of data analytics and wanting to become hands-on with R programming.

Why is this course Unique?

1. Unique Curriculum : The course is designed to make one proficient in data analytics tool R , starting from the fundamentals and going onto application of the tool R which is one of the most widely used tools in the industry.

2. Extensive hands-on skills : The student will gain competence through hands-on learning in the latest technologies and tools, which are being widely used in the Data Analytics industry.

3. Online learning : Apart from the regular ILT classroom sessions, the students will have access to online learning and practice on Training.com and NIIT student app.

4. Hands-on Learning : Learners need to bring their laptops to the classroom for the session, where apart from the fundamental theoretical knowledge they are also given hands-on practice on various digital marketing scenarios.

What learning resources are available?

Expert Faculty: Experienced faculty in related field interacting with students. Apart from guiding students on concepts and its implementation, he will pose challenges to learners to think through all the topics leading to better learning and increased retention.

Study Material: Students will be provided with advanced study materials in form of e-books.

Hands-on Learning: Apart from classroom hours, learners will be provided with dedicated machine room hours to practice their learnings on R.

What are the pre-requisites for the enrolment?
  • Have basic knowledge of working in the Windows environment and Microsoft excel
  • Knowledge of Maths/Statistics upto Class XII
What certificates will I be receiving for this course?

1. Graded Certificate

Only students successfully completing the program with a CWAP of >=50% will be awarded the Graded Certificate in “Programming in R”

2. Participation Certificate

Students who do not appear for the Appraisals or score a CWAP of < 50% will be issued only a Participation Certificate in “Programming in R”.

What will be my takeaway from this course?

At the end of this program, the learner will be able to:

i. Getting hands-on with R Programming

ii. Working with datasets in R

iii. Visualization of data in R

 

Appointment with counsellor