Types and Methods of Data Analysis: A Comprehensive Guide


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Data Analysis is a term that can sound pretty weighty and complex. But if we simply spell it out, it is the process where raw data is compiled, processed, filtered, and channelized using technology to gain useful insights that will profitably steer an enterprise. In this blog, we discuss the kind of data analysis methods and it’s types.

But ever wondered how does so much data get filtered and reach the right audience? All thanks to the wizardry ongoings delivered by ‘Data Analysts’ and ‘scientists’. Data Analysis has become one of the most sought-after professions across the globe. Finance, Retail, Insurance, Telecom, Banking, IT – all industries keep hiring specialists that have adept knowledge of Analysing Data.


The Methodology

Getting into the basics one must understand how is Data Analysis done. The best way to understand is to remember GPEU (an acronym coined by us ☺). GATHER + PROCESS + EXPLORE + USE. But like it is said ‘God is in Detail’. Below is a more detailed explanation for this. Following is the process of Data Analysis:

  • The objective of Analysis: Every task is done with an objective. Firstly one needs to get answers to WHY? – What is the purpose of this analysis that will help you advance towards your goal

WHAT? – What kind of data are you planning to analyze

HOW? – How will you analyze using the right methodology

  • Once the objective is defined one needs to collate all the raw data basis the requirement checklist. This raw data is received from various sources like surveys, case studies, reports, interactions, direct contact or groups.
  • Next is the filtering of data. After the collection of raw data, it is important to filter it before it reaches the Analyst desk. Cleansing of data involves the removal of duplicate data, unwanted/irrelevant content, and errors.

  • Now the job of Analysts begins. After the data filtration, an analyst feeds the data into analyzing software with which he can understand and derive results that are best suited for the organization. Some of the popular tools that Analyst relies on include Excel, Python, Metabase etc to name a few.
  • With further filtration by the Analysts the data results are evaluated and they decide the best ways to put this data to use. It is all about ‘Picture paint’. The best ways in which data is picture painted is through graphs, videos, bulleted points, charts, and other such tools which make the data interesting and easy to understand for the end-user.

Data Analysis is a big umbrella. There are several techniques that analysts depend on getting the best results. Some of them are:

  • Machine Learning
  • Regression Analysis
  • Content Analysis
  • Algorithms
  • Sentiment analysis


Everyone uses different terminology. One uses “data analysis methods” and one uses “data analysis techniques”. It is one and the same thing. By now organizations have realized the importance of Data Analysts. This is a very niche space and holds a lot of scope for growth. With technology rapidly advancing and hoards of data being produced there is a huge demand for data analysts. Even organizations are hugely investing in upskilling of employees to create valuable resources. Even educational institutions have curated specialized full-time and part-time courses to fulfill this demand.


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