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KEY FEATURE

It is a method of analysing data sets in order to summarise their main characteristics, which is frequently done using statistical graphics and other data visualisation methods.

A statistical model may or may not be used, but the primary goal of EDA is to see what the data can tell us beyond formal modelling and thus contrasts traditional hypothesis testing.

Since 1970, John Tukey has promoted exploratory data analysis to encourage statisticians to explore the data and possibly formulate hypotheses that could lead to new data collection and experiments.

EDA differs from initial data analysis (IDA), which focuses on checking assumptions required for model fitting and hypothesis testing, as well as handling missing.

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Key Topics

    What Exactly Is Exploratory Data Analysis?

    Exploratory data analysis is a statistical method of analysing data sets in order to summarise their key features. Data visualisation and graphical representation techniques were commonly used in exploratory data analysis. Exploratory data analysis allows us to see what the data can tell us beyond formal modelling and thus contrast traditional hypothesis testing. It assists in determining how to best manipulate data sources to obtain the answers you require, as well as making it easier for data scientists to identify patterns, spot anomalies, test a hypothesis, or check assumptions.
    Exploratory data analysis differs from initial data analysis in that it focuses on checking the assumptions needed for model fitting and hypothesis testing, dealing with missing values, and transforming variables as needed. EDA includes IDA.

    EDA's goals are as follows:

    • Allow for unexpected data discoveries.
    • Propose hypotheses for the causes of observed phenomena.
    • Examine the assumptions that will be used to make statistical inferences.
    • Assist in the selection of suitable statistical tools and techniques
    • Create a foundation for additional data collection through surveys or experiments.

     

    Exploratory Data Analysis Type

    • Univariate Non-Graphical: This is the most basic type of data analysis, with only one variable being analyzed.

     

    • Non-Graphical Multivariate: It has more than one variable. Cross-tabulation or statistics are commonly used in this EDA technique to demonstrate the relationship between two or more variables of data.
    • Multivariate graphical: It displays relationships between two or more sets of data using graphics. It is a graphical method with a single variable. Graphically represent the data.

    Data Analysis Techniques And Tools

    There are numerous data analysis tools available.
    • Box plot
    • Histogram
    • Heat map
    • Bar chart
    • Scatter plot
    Dimensionality Reduction :
    • Principal component analysis (PCA)
    • Multilinear PCA

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