Data analytics

Sathish M (Management Accountant)     07 March 2016

Sathish M
Management Accountant 
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Data analytics (DA) is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is used in many industries to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing models or theories. Data analytics is distinguished from data mining by the scope, purpose and focus of the analysis. Data miners sort through huge data sets using sophisticated software to identify undiscovered patterns and establish hidden relationships. Data analytics focuses on inference, the process of deriving a conclusion based solely on what is already known by the researcher.



The science is generally divided into exploratory data analysis (EDA), where new features in the data are discovered, and confirmatory data analysis (CDA), where existing hypotheses are proven true or false. Qualitative data analysis (QDA) is used in the social sciences to draw conclusions from non-numerical data like words, photographs or video. In information technology, the term has a special meaning in the context of IT audits, when the controls for an organization's information systems, operations and processes are examined. Data analysis is used to determine whether the systems in place effectively protect data, operate efficiently and succeed in accomplishing an organization's overall goals.


The term "analytics" has been used by many business intelligence (BI) software vendors as a buzzword to describe quite different functions. Data analytics is used to describe everything from online analytical processing (OLAP) to CRM analytics in call centers. Banks and credit cards companies, for instance, analyze withdrawal and spending patterns to prevent fraud or identity theft. Ecommerce companies examine Web site traffic or navigation patterns to determine which customers are more or less likely to buy a product or service based upon prior purchases or viewing trends. Modern data analytics often use information dashboards supported by real-time data streams. So-called real-time analytics involves dynamic analysis and reporting, based on data entered into a system less than one minute before the actual time of use.



Ravi Kumar (Professional Blogger)     25 March 2019

Ravi Kumar
Professional Blogger 
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Huge Data is increasingly receiving noticeable interest throughout the world, as companies, overall verticals like retail, utilities, pharmaceuticals, media, vitality, and others are grasping the most current IT idea. This is also the reason why you should enroll for best data analytics course in Noida and certifications in big data have become so popular in recent years.


  • Collect data from different sources
  • Organize the data that holds value
  • Generate & present in-depth reports
  • Look for correlations, patterns, trends
  • Generate ideas for process improvement


A data analytics professional has a wide range of job titles and areas from which to choose. Since big data is used almost everywhere today, you can pick to be a:

  • Metrics and Analytics Specialist
  • Big Data Engineer
  • Data Analyst
  • Data Analytics Consultant

These are just some of the job titles you could hold in big corporations such as ITrend, IBM, Opera, Oracle, etc and the possibilities are endless.

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