Exploratory Data Analysis – Importance, Types and Methods

Data analysis is an integral aspect of academic research. There are different types of data analysis techniques and methods. But one of the most effective techniques is exploratory data analysis. Exploratory data analysis allows the researchers to explore and investigate the data. They can summarise the fundamental attributes of data with it. This article will explain exploratory data analysis and its importance, types, and methods.

What Is Exploratory Data Analysis?

Researchers and data scientists use exploratory data analysis to study and explore data sets. It helps them summarise the primary properties of the data. It also involves data visualisation, leading to a better understanding of data sets. Data scientists can manipulate data sets using it to obtain the required answers. It enables them to identify trends and patterns in the data sets.

Furthermore, it helps data scientists and researchers to test hypothesis and verify the assumptions. Exploratory data analysis seeks to gain a deeper understanding of the data beyond the traditional confines of data modelling and hypothesis testing. Researchers can gain better insights into data variables and their interactions. It also assists them in deciding which statistical techniques for the data analysis will be suitable. Also it helps researchers in detecting anomalies in the data sets. Researchers can detect the outliers in data sets with the help of exploratory analysis. They have also an option to hire best dissertation writing services in case of any issues.

What Are The Aims And Objectives Of Exploratory Data Analysis?

Exploratory data analysis helps researchers achieve the following aims and objectives:

  • Identifies the errors and outliers in the data sets
  • Verifies the assumptions
  • Helps in deciding the appropriate statistical methods for analysing the data sets.
  • Identifies trends and patterns between the variables in the data sets
  • Visualises data sets and gains a deeper understanding of the data sets

What Is The Importance Of Exploratory Data Analysis?

Exploratory data analysis is an important component of data analysis. It helps researchers explore data sets and analyse them deeply. The purpose of the exploratory analysis is to determine the trends and patterns in the data sets. Furthermore, it helps identify errors and anomalies before making any assumptions. Researchers identify whether the data sets correspond with the initial assumptions and real properties of the phenomena they are examining. It significantly enhances the understanding of the researchers regarding data sets. Researchers can spot data distributions, variations, data features and patterns in the data sets.

The primary aim of data analysis is to determine relationships between the variables. Researchers can find interesting connections between variables with the help of exploratory data analysis. It helps researchers feel confident that the results from the data set are reliable and correspond to the initial assumptions. Researchers can formulate better statistical models and techniques with the help of exploratory analysis. Also, researchers can refine the data sets by visualising them and removing anomalies and errors. It assists data scientists in devising statistical models for machine learning and accurately predicting future outcomes.

What Are The Different Types Of Exploratory Data Analysis?

There are four types of exploratory data analysis which are as follows:

  • Univariate non-graphical
  • Univariate Graphical
  • Multivariate Non-Graphical
  • Multivariate Graphical

Univariate Non-Graphical

Univariate non-graphical is the most basic type of exploratory data analysis. It is because it involves only one variable. The single variable can be about age and gender. The univariate non-graphical technique is an assessment of the sample distribution of the data from the data sample. Researchers use this technique to understand the sample distribution. It also encompasses the detection of outliers in the data set. Researchers assess the sample distribution using central measures of tendency such as mean, median and mode. It helps them identify the data’s centre, spread, modality and shape. This technique also involves dispersion measures such as variance analysis, standard deviation, and interquartile range. The spread helps researchers determine the distribution of data in the data set.

Univariate Graphical

Univariate graphical analysis complements the univariate non-graphical technique. It involves the visual representation of data. Researchers visualise the distribution of data from the data set. The use of histograms is common in univariate graphic analysis. The histogram showcases the frequency and proportion of values in the data set. Histograms help researchers visually identify the distribution of data sets and make the data exploration process easier. Researchers can better understand measures of central tendency, spread, modality, shape, and outliers in the data. Boxplots are also more frequent in univariate graphical analysis. It helps researchers visualise the data about central tendency and skewed distribution.

Multivariate Non-Graphical

The multivariate non-graphical data analysis technique is more frequent when there are two or more than two variables. It helps researchers identify the links and connections between the variables. Researchers use cross-tabulation or statistical techniques to identify the relationship between the variables. They also use statistics to compare categorical variables (exploratory variable) with the quantitative variable (outcome variable). Researchers make comparisons to identify trends and variations between the variables. It also involves analysis of variance. Statistical techniques for analysing the correlations between variables are also common in this technique.

Multivariate Graphical

The multivariate graphical technique is efficient for visualising categorical multivariate quantities. The most frequent technique is the usage of a bar plot. Each group in the bar plot showcases a single variable, and each bar within the group represents the level of other variables. Researchers can make parallel boxplots for each categorical variable. Also, for two variables, researchers can use the scatterplot technique. In a scatterplot, the x-axis represents one variable, and the y-axis represents the second variable. The explanatory variable is shown on the x-axis. 

Conclusion

Exploratory data analysis is a systematic review of data. It explores, analyses and investigates the data thoroughly. Researchers use this technique to identify errors and outliers and verify the assumptions. It is an effective way of identifying the trends and patterns in the data set. Also, it helps researchers choose the appropriate statistical techniques for data analysis. It refines the data and situates it within a proper context.

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