Exploratory Data Analysis
Matthieu Komorowski, Dominic Charles Marshall, Justin D. Salciccioli, Yves Crutain · 2016
Exploratory data analysis (EDA) is an essential step in any research analysis. The primary aim with exploratory analysis is to examine the data for distribution, outliers and anomalies to direct specific testing of your hypothesis. It also provides tools for hypothesis generation by visualizing and understanding the data usually through graphical representation [ 1 ]. EDA aims to assist the natural patterns recognition of the analyst. Finally, feature selection techniques often fall into EDA. Since the seminal work of Tukey in 1977, EDA has gained a large following as the gold standard methodology to analyze a data set [ 2 , 3 ]. According to Howard Seltman (Carnegie Mellon University), “loosely speaking, any method of looking at data that does not include formal statistical modeling and inference falls under the term exploratory data analysis” [ 4 ].