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Comprehensive R Programming
Comprehensive R Programming” programme has been designed & developed to cater to the requirements of students, researchers, and professionals across industries

60 Hours programme

Contact hours: 36hours
  • Conceptual Learning
  • Hands-on Learning
  • Projects/ Assignments
For Enrollment
Programme Objectives
R is the most popular open-source Analytics software, increasingly being deployed across the globe and across industries & business functions. R provides a range of functionalities & features readily applicable across industry verticals and business horizontals. From business intelligence to analytics, from business forecasting to text mining, from banks to pharmaceutical companies – R today caters to all.

The “Comprehensive R Programming” programme equips participants with the basics as well as advanced skills in the R programming environment. The programme introduces participants to the R environment and the world of R programming, and takes the learner forward to become anskilled R programmer. The programme imparts strong skills to perform data management, data manipulation, data transformation, data summarization, and data visualization using R.
Who should go for the Programme?
The “Comprehensive R Programming” programme has been designed & developed to cater to the requirements of students, researchers, and professionals across industries (retail, banking, technology, telecommunications, media, airlines, automotive, manufacturing, etc) as well as across business functions (marketing, finance, operations, supply chains, human resources, etc). The programme offers substantial value to all participants aspiring to carve out a career for themselves in the high-demand & rapidly-growing Analytics industry, where R is fast-becoming an extremely powerful & popular tool.

Professionals in the traditional IT/ITES industry, where applications of R are getting very popular, would also benefit from the programme. For UG/PG students looking for an entry into the Analytics industry, the programme offers an opportunity to rapidly upskill oneself in the highly-prized R skillset, thereby scoring a significant edge over their

Key Takeaways

  • Gain knowledge of & expertize in various aspects of the R environment
  • Articulate the functionalities & capabilities of R as applied to world of Business Analytics
  • Acquire proficiency in data management, data manipulation, data transformation, data summarization, and data visualization using R
  • Appreciate the various features available in different R packages and the various R libraries
  • Appreciate the various features in R available for advanced functionalities like Optimization and Integration

Learning Mode

Curriculum Overview

Overview of the R Software
A brief history of R
Contributors to R
R Community & R Ecosystem
A comparative view of R vis-à-vis SAS, SPSS, etc
Installation of the R Software
How to install R?
The R Studio integrated development environment
The R Graphic User Interface
Popular R Packages
Installation of R Packages
The R Interface
R Studio interfaces
R GUI interfaces
The R Workspace
Customizing the start-up process in R
R in UNIX
Batch Processing in R
Datasets in R
Variables in R
Observations in R
Data Scalars, Arrays & Matrices in R
Process Flow in R
R Programming preliminaries
Libraries in R
Functions in R
Defining/ Creating Datasets in R
Data Types in R
Data Imports & Data Exports
Reading/ Editing Data in R
Detection of missing data in R
Labelling of variables in R
Renaming variables in R
Data Type conversions in R
Operators in R
Controls in R
Defining/ Creating variables in R
Built-in Functions in R
User Defined Functions in R
Sorting Datasets in R
Splitting/ Merging Datasets in R
Missing Value Imputation in R
Summarizing Functions in R
Data Tabulation in R
Bivariate Data Analysis in R
Scatter Plots
Bar Charts
Line Plots
Pie Charts
Histograms
Density Charts
Box Plots
Publishing the R Output through html/ pdf files
Publishing R graphs/ charts
Using the R Output for further analyses
Publishing the R Output throughHandling Duplicate Data in R
Sampling the Data with R
Cumulative Commands in R
Traversing the Data with the 'apply' family of Functions
Formulae interfaces in R
Adding more arguments to Functions in R
Scoping of Objects and Functions in R
Interaction Plots
Co Plots
Strip Charts
Index Plots
Special Plots
Design Plots
Bubble Plots
Adding shapes to a plot in R

Are there any Learning Pre-requisites?

There are no specific learning pre-requisites for the programme. The programme expects NEITHER a strong knowledge of Statistics NOR an advance knowledge of computer programming knowledge, in advance.

Effectiveness of learning through the programme would be enhanced if participants possess good logical & analytical reasoning skills, apart from a basic familiarity with broad computer programming concepts like variables, operators, if-then-else conditions, loops, etc.

Enrollment

Comprehensive R Programming

MEET Our Consultants & Faculty

Goutam Das

M. Tech. (Computer Engineering), IIT Kharagpur

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Dr Nidhan "Neal" Choudhuri

Ph.D. (Statistics), Michigan State University

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Dr Chiranjit Acharya

B.E., M.E., & Ph.D. (Computer Science), Jadavpur University

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Sangeet Pal

PGDM (MBA), IIM Ahmedabad

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Pravin Bhosale

B.E. (Mechanical Engineering), Shivaji University, Maharashtra

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Anirban Bhaduri

B.E. (Computer Science), IIEST Calcutta (e.k.a. Bengal Engineering & Science University)

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Shyam Karmakar

Chartered Statistician at The Royal Statistical Society, London

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Shamik Choudhury

M. Tech. (Computer Science), Indian Statistical Institute, Calcutta

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