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Experts Guide To Understanding Data Analysis & Data Analytics
Data analysis and data analytics are two distinct technologies that hold slightly different meanings. The primary difference between these two technologies is the matter of scale & to be clearly precise, data analysis is a subcomponent of data analytics.
On the outline, Data analysis is the process of examining, transforming and arranging the available data in hand to get a clear idea about its insights & to extract the relevant information. Coming to Data analytics, it is a much broader technology that encompasses complete data management techniques. In addition to data analysis techniques it also includes collection, organisation, data storage operations & their relevant tools and techniques.
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Before differentiating Data Analysis & Data Analytics let us first understand what exactly these concepts are.
Understanding Data Analysis & Data Analytics-
The process of data analysis is like separation of a whole into its component parts whereas data analytics is more like a process of logical analysis. Enterprises can make use of both these technologies to drive effective decisions for their business, find the unknown correlations for identifying the trends & for better understanding their customer needs.
The concept of Data Analysis is all about interpreting with the data to explore the hidden insights & to find relation between unknown patterns so as to recommend conclusions that would help in taking accurate decisions.
Extensively used Data Analysis tools are Open Refine, Tableau public, KNIME, Google Fusion Tables, Node XL and many more.
Data Analytics is all about making use of Machine Learning models, Statistical Analysis & as well as computer-based models to dig deep into the data for extracting clear insights for taking better informed decisions from the data.
It can be interpreted as the process of transforming data into actions through analysis so as to help the organizations through accurate decisions & and problem-solving. Analytics makes extensive use of tools like as Microsoft Excel, SAS, R, Python(libraries), Tableau, Apache Spark & more.
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