Course Details

Using R for Data Visualization and Analytics


About The Course:

Today we live in Data Age. Data is all around us. We continuously generate as well as consume data in various formats, and sizes from number of varied sources. This data can be a big asset if stored, processed and analysed efficiently in real time. Such processed and analysed data can give companies valuable insights leading to competitive advantage, efficient service delivery and above all customer satisfaction.

Data Science refers to theories, technologies, processes and systems used to extract inferences/insights/inherent trends from data in various forms (structured or unstructured) coming from variety of sources. Data analysis and visualisation lie at the core of any data science project. Now-a-days many tools and technologies, proprietary or free/open source, are available that can be readily used for these purposes.

This course introduces R – a language and environment for Statistical Computing and Visualization. In recent years, R has become very popular due its open source cross-platform nature, robust package repository and strong graphics capabilities. During the course, one will not only learn about basics of R, but also about techniques of data acquisition and processing. Course will also cover in detail the features of R related to data analysis and visualisation. After the course, one should be able to use R extensively in his/her data analysis tasks.

After the course, one will be able to use R extensively in his/her data analysis tasks.

Objectives: On completion of the course, one should be able:

  • To understand the role and use of R with respect to data analysis and visualisation.
  • To understand R environment, packaging system etc.
  • To apply R for data acquisition, cleaning, exploratory and statistical analysis etc.
  • To document and report results of analysis using R markdown.

Target audience: Researchers, students, teachers/lecturers of science & technology, People from Industry and government sectors working in the domain.

Topics to be covered:

  • R basics
  • Data acquisition, handling, representation and pre-processing.
  • Data visualisation
  • Statistical analysis in R
  • R markdown
Mode Duration Dates
Classroom and Lab 3 days, 15-18 hours (Classroom) and 6-8 hours (lab) May 19 - 21, 2016