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Exploratory data analysis (EDA) is a term for certain kinds of initial analysis and findings done with data sets, usually early on in an analytical process. Some experts describe it as “taking a peek” at the data to understand more about what it represents and how to apply it. Exploratory data analysis is often a precursor to other kinds of work with statistics and data.
Professionals will often use various visual tools to do exploratory data analysis, for example, to test an intuitive hypothesis, and figure out in what ways data sets are similar or different. One excellent example is the use of a scatter plot graph – this simple bit of exploratory data analysis can show analysts whether there is a trend or major difference between two or more data sets, by making numbers, which are relatively hard for the human brain to analyze as a whole, into easy visuals. Block graphs and line graphs are other examples of this type of quick exploratory analysis. Those who are working with the data can expedite the process of figuring out what the data means, what it can be used for, and what conclusions can be drawn from it.