Data analysis is the technique of analyzing and inspecting as well as cleansing and transforming of data to retrieve useful information or suggest a solution and this process helps in making decisions for business or other processes. In this tutorial, you will be learning about the various types of data analysis and their uses.

How is Data Analyzed?

Data analysis of qualitative research consists of statistical methodologies which analyze data iterative and easier to compare based on values. Other than that data scientists and analysts search for patterns in the various observations in data.

Different Types of Data Analysis

Data analysis can be categorized into six major types. These are:
  • Descriptive
  • Inferential
  • Predictive
  • Causal
  • Mechanistic

Every data analysts and data scientists should know at least these types of analyzing of data.


Approximately 90% of the companies and organization follow this approach of the data analysis. It is a quantitative approach which elaborates the primary feature in data collection and informs back to the analyst with one crucial answer - "What has Happened?" in details. The primary focus of using this technique of data analysis is to figure out the grounds and causes behind the expensive rise or fall of business in the past and its solution for future. It is mostly used in BI (Business Intelligence) and data mining field.


This technique aims in testing theories about the behavior and nature of any data depending on some sample "subjects" or "results" taken from observations. It reflects the goal of statistical models, and it depends mostly on the population of data as well as the sampling schemes.


This technique of analytics adds the flavor of controlling the future demands and accessing risks. Moreover, it directs the various possible outcomes which are likely to enhance the key business. It mainly deals with "What should the business performance to accomplish certain goals?"

This type of analysis uses advanced concept depending on:

  1. Optimizing for achieving the finest upshot.
  2. A stochastic optimization which helps in understanding the way of achieving and identifying data qualms for making enhanced decisions.


This technique deals with finding out the outcome of change in a variable (dependent) when there is a change in another variable (independent). And implementing these type of analysis of data need randomized studies over data.


This analysis technique deals with the understanding of precise change in variables (independent) which may lead to change in other variables (dependent) also. These type of analysis of data are hard to infer.

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