"Exploratory data analysis (EDA) is a collection of techniques that reveal (or search for) structure in a data set before calculating any probabilistic model. Its purpose is to obtain information about the data distribution (univariate or multivariate), about the presence of outliers and clusters, to disclose relationships and correlations between objects and/or variables." (Ildiko E Frank & Roberto Todeschini, "The Data Analysis Handbook", 1994)
"Processes and methods for exploring patterns and trends in the data that are not known prior to the analysis. It makes heavy use of graphs, tables, and statistics." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2007)
"The process of analyzing data to suggest hypotheses using statistical tools, which can then be tested." (DAMA International, "The DAMA Dictionary of Data Management", 2011)
"In statistics, exploratory data analysis is an approach to analyzing datasets to summarize their main characteristics, often with visual methods." (Keith Holdaway, "Harness Oil and Gas Big Data with Analytics", 2014)
"Process in which data patterns guide the analysis or suggest revisions to the preliminary data analysis plan." (K N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)
"Exploratory Data Analysis is about taking a dataset and extracting the most important information from it, in such a way that it is possible to get an idea of what the data looks like." (Richard M Reese et al, Java: Data Science Made Easy, 2017)
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