Exploratory Data Analysis - meaning and definition. What is Exploratory Data Analysis
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What (who) is Exploratory Data Analysis - definition

APPROACH OF ANALYZING DATA SETS IN STATISTICS
Exploratory analysis; Explorative data analysis; Exploratory statistics; Exploratory Data Analysis
  • Data science process flowchart

Exploratory Data Analysis         
(EDA) [J.W.Tukey, "Exploratory Data Analysis", 1977, Addisson Wesley].
Exploratory data analysis         
In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling and thereby contrasts traditional hypothesis testing.
Data analysis         
ACTIVITY FOR GAINING INSIGHT FROM DATA
Data Analysis; Data analyst; Data Interpretation; Data-analysis; Information analysis; Data Analytics; Free software for data analysis; Algorithms for data analysis; Analyze data
Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains.

Wikipedia

Exploratory data analysis

In statistics, exploratory data analysis (EDA) is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling and thereby contrasts traditional hypothesis testing. Exploratory data analysis has been promoted by John Tukey since 1970 to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.