Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software. Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.

Topics to be covered in Workshop (20% Theory & 80% Hands-On Session)


  • History of R
  • Advantages and disadvantages
  • Downloading and installing
  • How to find documentation


  • Using the R console
  • Getting help
  • Learning about the environment
  • Writing and executing scripts
  • Saving your work

Installing Packages

  • Finding resources
  • Installing resources

Data Structures, Variables

  • Variables and assignment
  • Data types
  • Indexing, subsetting
  • Viewing data and summaries
  • Naming conventions
  • Objects

Getting Data into the R Environment

  • Built-in data
  • Reading data from structured text files
  • Reading data using ODBC

Control Flow

  • Truth testing
  • Branching
  • Looping
  • Vectorized calculations

Functions in Depth

  • Parameters
  • Return values
  • Variable scope
  • Exception handling

Handling Dates in R

  • Date and date-time classes in R
  • Formatting dates for modeling

Descriptive Statistics

  • Continuous data
  • Categorical data

Inferential Statistics

  • Bivariate correlation
  • T-test and non-parametric equivalents
  • Chi-squared test
  • Distribution testing
  • Power testing

Group By Calculations

  • Split apply combine strategy

Base Graphics

  • Base graphics system in R
  • Scatterplots, histograms, barcharts, box and whiskers, dotplots
  • Labels, legends, Titles, Axes
  • Exporting graphics to different formats

Advanced R Graphics: GGPlot2

  • Understanding the grammar of graphics
  • Quick plot function
  • Building graphics by pieces

Linear Regression

  • Linear models
  • Regression plots
  • Confounding / Interaction in regression
  • Scoring new data from models (prediction) 


  • We will use Rstudio and Rpackage for the Practice
  • We will cover one real time project ex : Patanjali product analysis, Snapdeal analysis.. 

Duration: The duration of this workshop will be two consecutive days, with 6-7 hour session each day in a total of 12-14 hours.

 Certification Policy:

  • Certificate of Merit for all the workshop participants.
  •  At the end of this workshop, a small competition will be organized among the participating students and winners will be awarded with a 'Certificate of Excellence'.
  • Certificate of Coordination for the coordinators of the campus workshops.

Eligibility:  It's a basic level workshop so there are no prerequisites. Anyone interested, can join this workshop.

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