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Exploratory Data Analysis was first proposed by John Tukey of Princeton University in the 1960s as a set of techniques designed to uncover potential relationships and patterns within data. Whether you’re new to data analysis, a business professional, or a machine learning enthusiast, understanding your data is the first step toward making smarter decisions.
What is Exploratory Data Analysis [EDA]?
Exploratory Data Analysis (EDA) is the process of examining and visualising relevant datasets to identify patterns, relationships, and anomalies before performing formal statistical analysis or modelling.
In simple words, Exploratory Data Analysis (EDA) is a method used to analyse and summarise datasets in order to understand their main characteristics, instead of performing the final analysis. It involves the use of statistical techniques and graphical tools to identify patterns, detect anomalies, and check assumptions before applying advanced analytical models.
New to Data Science? You’re in the right place! This tutorial is part of our Data Science Fundamentals series, created to help beginners grasp key concepts step by step, with simple language and real-world examples.
Key Features of Exploratory Data Analysis [EDA]
EDA focuses on:
- Understanding the structure of data.
- Identifying missing values and errors.
- Detecting outliers.
- Discovering patterns and relationships.
Importance of Exploratory Data Analysis in Data Science
Exploratory Data Analysis (EDA) is important because it helps analysts understand data before making assumptions or applying models. It identifies errors, missing values, and outliers, ensuring better data quality.
Moreover, EDA reveals patterns and relationships between variables, making results more reliable and useful for decision-making. It also ensures that the analysis aligns with business goals.
Finally, EDA prepares data for advanced techniques like statistical modelling and machine learning.
Types of Exploratory Data Analysis (EDA) Based on the Number of Variables
Exploratory Data Analysis (EDA) can be classified into three main types based on the number of variables involved in the analysis.
Univariate Analysis
This type focuses on analysing a single variable at a time. It is used to understand the basic characteristics of the data, such as distribution, central tendency, and variation. Common tools include frequency tables, bar charts, histograms, mean, and standard deviation.
Bivariate Analysis
This type involves the analysis of two variables to identify relationships or associations between them. Techniques include two-way tables, scatter plots, correlation analysis, and box plots.
Multivariate Analysis
This type deals with more than two variables simultaneously. It is used to understand complex relationships within the dataset. Techniques include clustering, dimensionality reduction (such as PCA), and advanced visualisations.
While EDA is primarily categorised by the number of variables (Univariate, Bivariate, Multivariate), it can also be viewed through the lens of methodology, splitting tasks into Graphical and Non-Graphical approaches.
Types of Exploratory Data Analysis (EDA) Based on the Method of Analysis
In statistical and research contexts, EDA is also classified based on how the analysis is performed.
Univariate Non-Graphical Analysis
This involves analysing a single variable using numerical measures such as mean, median, and standard deviation. It helps summarise the data without using any visual tools.
Univariate Graphical Analysis
This uses charts and plots, such as histograms and bar charts, to visualise the distribution of a single variable. It makes patterns and trends easier to understand.
Multivariate Non-Graphical Analysis
This focuses on analysing relationships among two or more variables using statistical methods such as correlation or covariance. It provides numerical insights into how variables are related.
Multivariate Graphical Analysis
This uses visual tools such as scatter plots, box plots, and bubble charts to explore relationships among multiple variables. It helps in identifying patterns, clusters, and outliers visually.
Steps in Exploratory Data Analysis
Exploratory Data Analysis (EDA) is a step-by-step process used to understand data, detect patterns, and prepare it for further analysis.
Data Understanding
First, understand the dataset by identifying:
- Types of variables (categorical or numerical).
- Size and structure of data.
- Source and quality of data.
This step ensures the data is suitable for analysis.
Data Cleaning
Next, improve data quality by:
- Handling missing values
- Removing duplicates
- Correcting errors and inconsistencies
Clean data leads to accurate results.
Univariate Analysis
Analyse each variable individually:
- Use frequency tables for categorical data.
- Calculate the mean, median, and standard deviation for numerical data.
- Draw histograms or bar charts.
This helps understand distribution and basic characteristics.
Bivariate Analysis
Study relationships between two variables:
- Use scatter plots and correlation.
- Create two-way tables.
- Compare using box plots.
This helps identify patterns and associations.
Multivariate Analysis
Examine relationships among multiple variables:
- Use clustering techniques.
- Apply dimensionality reduction (PCA).
- Use advanced visualisations.
Helps understand complex data relationships.
Data Visualisation & Interpretation
Finally, present insights using charts and graphs and interpret the findings for decision-making.
This converts data into meaningful insights.
Exploratory Data Analysis (EDA) Languages
Exploratory Data Analysis (EDA) is performed using various programming languages and tools that help in data cleaning, analysis, and visualisation.
Here are the common languages used for Exploratory Data Analysis:
Python
Python is a high-level, object-oriented language, the most widely used for EDA due to its simplicity and powerful libraries, such as:
- Pandas (data manipulation).
- Matplotlib and Seaborn (data visualisation).
It is beginner-friendly and widely used in data science and machine learning.
R Programming
An open-source language designed for statistical computing and data visualisation. It is widely used by statisticians for data analysis and generating insights.
- Strong in statistical computations.
- Excellent visualisation libraries like ggplot2.
Preferred in research and academic fields.
Wrapping Up – What is Exploratory Data Analysis [EDA]
Exploratory Data Analysis (EDA) is a crucial step in data science that helps in understanding data, identifying patterns, and ensuring data quality before applying advanced techniques. It forms the foundation for accurate analysis and effective decision-making.
We have also covered the key features of EDA, its importance in data science, the types of EDA based on the number of variables and the method of analysis, the steps involved in EDA, and the commonly used EDA languages.
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