Data Analytics Techniques

Some analytical techniques are required in order to the attempt some relevant information. It gives a broad overview of some of the most commonly used techniques and technologies to help the reader to better understand the tools based on big data analytics. There are many analytic techniques that could be employed when considering a big data project. Which ones are used that depends on the type of data being analyzed, the technology available to you, and the research questions you are trying to solve? 

1 Data Mining

 Data mining allows extraction of useful information from raw datasets and visualization of the same in a manner that is helpful for making decisions. Commonly used data mining techniques include classification, regression analysis, clustering, machine learning and outlier detection. In order to analyze different variables and how they are dependent on one another, regression analysis may be used. Companies commonly use this technique for analyzing CRM big data and evaluating varied levels of customer satisfaction and its impact on customer retention. Taking this analysis further, there may also be a need to cluster similar customers together to analyze their buying patterns or classify them on the basis of certain attributes. Clustering and classification are the techniques used for this purpose.

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Frontiers, E. (2023). Data Analytics Techniques. Afribary. Retrieved from

MLA 8th

Frontiers, Edu "Data Analytics Techniques" Afribary. Afribary, 29 Mar. 2023, Accessed 17 Jun. 2024.


Frontiers, Edu . "Data Analytics Techniques". Afribary, Afribary, 29 Mar. 2023. Web. 17 Jun. 2024. < >.


Frontiers, Edu . "Data Analytics Techniques" Afribary (2023). Accessed June 17, 2024.