Data Analytics


A data centre is a facility that centralizes an institute Research and Development operations, stores, manages and disseminates its data, projects and training. Data centres house a network’s most critical systems and are vital to the continuity of daily operations in every grassland data.


Data analytics (DA)

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. Data analytics technologies and techniques are widely used in commercial industries to enable organizations to make more-informed business decisions and by scientists and researchers to verify or disprove scientific models, theories and hypotheses.

Types of data analytics applications

At a high level, data analytics methodologies include exploratory data analysis (EDA), which aims to find patterns and relationships in data, and confirmatory data analysis (CDA), which applies statistical techniques to determine whether hypotheses about a data set are true or false. EDA is often compared to detective work, while CDA is akin to the work of a judge or jury during a court trial — a distinction first drawn by statistician John W. Tukey in his 1977 book Exploratory Data Analysis.

Data analytics can also be separated into quantitative data analysis and qualitative data analysis. The former involves the analysis of numerical data with quantifiable variables that can be compared or measured statistically. The qualitative approach is more interpretive — it focuses on understanding the content of non-numerical data like text, images, audio and video, including common phrases, themes and points of view.


  • To Update skill set in Data Science and Data Analytics among learning community.


  • To inspire students to be thinkers, designers and innovators in data science and emerging technologies.
  • To provide faculties and students platform for advancement in data science and data analytics for continuous improvement and growth.
  • To make easier for employers to recruit graduates to growing fields.

Centre Outcome

  • Improve students and faculty skill set in recent technology related with data analytics.
  • Ability to understand various kinds of tools, and apply mining techniques for realistic data.
  • Provide Domain knowledge with subject experts from industry and make ready for role of Business Analyst and Business Intelligence.
  • Gives exposures for becoming ready for entrepreneurship and startup.
  • Change behavior, capture opportunities, respond to threats, and improve skill.

Our Initiatives

  • Faculty Development
  • Skill Development
  • Research and Development
  • Industry-Institute Interaction

Our Partners

For more details, Click on to website