In data mining, Exploratory Data Analysis (EDA) is an approach to analyzing datasets to summarize their main characteristics, often with visual methods. EDA is used for seeing what the data can tell us before the modeling task. It is not easy to look at a column of numbers or a whole spreadsheet and determine important characteristics of the data. It may be tedious, boring, and/or overwhelming to derive insights by looking at plain numbers. Exploratory data analysis techniques have been devised as an aid in this situation.
Dear Rasha,
Exploratory data analysis refers to using a set of statistical tools to learn more about a data set before beginning classification. Perhaps this question relates mostly to the wording - these tools can be used for classification, but then they would not be EDA by definition.
In my experience, people refer to EDA as the process of generating figures and charts that make the data easier to interpret, which is usually done to help understand which algorithms would be best to implement.
Hello Rasha
I think that we have to use more and more Data Analysis and Data Visualization following this "Big Data" phenomena. I used unsupervised learning algorithm (k-Means) to extract new insights from enormous data in full text.
Similarly, professional BI (Business Intelligence) software like "Tableau" gives clear and powerful tools to make dashboards and storytelling project for decision makers.
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