1. Data Analytics :
Analytics is a technique of converting raw facts and figures into some particular actions by analyzing those raw data evaluations and perceptions in the context of organizational problem-solving and also with the decision making. Analytics is the discovery and conversation of significant patterns in data. Especially, precious in areas prosperous with recorded information, analytics depends on the simultaneous utility of statistics, computer programming, and operation lookup to qualify performance. Analytics frequently favors data visualization to talk insight. The aim of Data Analytics is to get actionable insights ensuing in smarter selections and higher commercial enterprise outcomes.
2. Data Analysis :
It is the technique of observing, transforming, cleaning, and modeling raw facts and figures with the purpose of developing beneficial information and acquiring profitable conclusions.
Difference between Data Analytics and Data Analysis :
|S.No.||Data Analytics||Data Analysis|
|1.||It is described as a traditional form or generic form of analytics.||It is described as a particularized form of analytics.|
|2.||It includes several stages like the collection of data and then the inspection of business data is done.||To process data, firstly raw data is defined in a meaningful manner, then data cleaning and conversion are done to get meaningful information from raw data.|
|3.||It supports decision making by analyzing enterprise data.||It analyzes the data by focusing on insights into business data.|
|4.||It uses various tools to process data such as Tableau, Python, Excel, etc.||It uses different tools to analyze data such as Rapid Miner, Open Refine, Node XL, KNIME, etc.|
|5.||Descriptive analysis cannot be performed on this.||A Descriptive analysis can be performed on this.|
|6.||One can find anonymous relations with the help of this.||One cannot find anonymous relations with the help of this.|
|7.||It does not deal with inferential analysis.||It supports inferential analysis.|
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