• Articles
  • Tutorials
  • Interview Questions

Exploratory Data Analysis In Data Science: A Step-by-Step Guide

Tutorial Playlist

Exploratory Data Analysis acts like your helpful friend, guiding you through the world of data and uncovering important information. This blog is your starting point for EDA, where you’ll learn about its importance. Come along as we explore the basics, check out its methods, and see how EDA turns data into useful insights so businesses can make smart decisions. 

Table of Contents

Dive into the world of data with our captivating Exploratory Data Analysis video!

What is Exploratory Data Analysis (EDA) in Data Science?

What is Exploratory Data Analysis (EDA) in Data Science?

Exploratory Data Analysis (EDA) is like playing detective with data. It’s the first thing you do when you want to understand a bunch of information. During EDA, you look at pictures and numbers to find interesting things and unusual stuff in the data. It’s like checking a map for hidden treasure before you start searching. EDA is the first and most important step in any data science adventure.

Enroll in Data Science Course Online to get all the insights on Data Science!!

Why is EDA Important?

The primary goal of EDA is to examine data without making any initial assumptions. It serves to uncover evident mistakes, gain a deeper understanding of data patterns, spot unusual occurrences or outliers, and reveal intriguing relationships between variables. Data scientists employ exploratory analysis to ensure that the results they produce are both reliable and relevant to the desired business objectives. 

EDA also aids stakeholders by confirming that they are asking the correct questions. It can address inquiries related to factors like data variability, categorical variables, and the reliability of results. Once the EDA process is finished and valuable insights are extracted, the features of this analysis can be employed for more advanced data investigations or applied in modeling and machine learning activities.

Steps to Perform Exploratory Data Analysis 

Steps to Perform Exploratory Data Analysis 

Exploratory Data Analysis (EDA) is a crucial step in the data science process that involves examining and understanding your data before jumping into modeling or making decisions. Here are the steps to perform EDA:

  • Step 1: Data Collection from Multiple Sources
    In today’s world, there is a vast amount of data being generated in different forms across various aspects of day-to-day life, such as education, sports, manufacturing, and tourism. Every business recognizes the value of making good use of this data through effective analysis. However, this relies on gathering the necessary data from multiple sources, including surveys, social media, and customer feedback, among others. Without acquiring enough pertinent data, it’s not possible to proceed with subsequent activities.
  • Step 2: Data Preparation to Perform Analysis
    EDA plays a role in cleaning and preparing data, similar to tidying up before beginning a task. Having clean data is crucial for precise analysis and modeling.
  • Step 3: Understanding the Outline of  Data
    EDA is like getting to know our data really well. It’s similar to checking a map before starting a trip. We look at how the data is spread out, if there are any clear patterns, and if there are any unusual things. This helps us understand what the data is trying to tell us.
  • Step 4: Formulating Hypotheses for Desired Result
    EDA helps us come up with questions or ideas based on our first look at the data. It’s a bit like planting the seeds for more research and building models. These initial questions become the starting point for deeper analysis and more detailed investigations, guiding us on our data journey.
  • Step 5: Division of Complex Data in Subcategories
    In EDA, data can be separated into meaningful sections based on specific criteria or characteristics. This segmentation helps us gain insights into distinct subgroups within the data and can lead to more targeted analysis.
  • Step 6: Effective Visualization of Data
    EDA uses visual methods to present statistics in graphical form. Visualizations like histograms, box plots, scatter plots, line graphs, heatmaps, and bar charts help spot patterns, trends, and connections in the data.
  • Step 7: Data-Driven Decision Making after Final Analysis
    In the end, EDA provides us with the information we need to make well-informed choices. It’s comparable to having a compass when exploring unknown terrain. Decisions based on data are more likely to result in successful outcomes.

Refer to these Data Science Interview Questions to ace your next interview!

Get 100% Hike!

Master Most in Demand Skills Now !

Types of Exploratory Data Analysis

Exploratory Data Analysis (EDA) is essential in data analysis. It helps you understand your data, find patterns, and discover important information. Some of the important types of Exploratory data analysis are described below:

  1. Univariate Analysis: Univariate data comprises a single variable. Univariate analysis is relatively simple because it’s focused solely on this individual variable. It doesn’t involve exploring how different variables are connected to each other. Instead, the primary goal is to depict the data and identify any existing patterns.
  2. Bivariate Analysis: In bivariate analysis, we are looking at how two things are connected. It helps us find relationships and connections and understand how one thing might affect another. We use tools like scatter plots, line graphs, correlation tables, and comparing data in tables to do this kind of analysis.
  3. Multivariate Analysis: Multivariate analysis in Exploratory Data Analysis (EDA) involves examining and analyzing multiple variables simultaneously to understand how they interact and influence each other. It aims to provide a deeper understanding of how a combination of variables collectively influences the data. It assists in uncovering complex patterns, relationships, and dependencies that may remain hidden when studying individual variables or pairs of variables separately.

Thinking of getting a master’s degree in Data Science? Enroll in the Master of Science in Data Science by the University of Essex.

Common EDA Techniques

There are many different EDA techniques that data scientists can use to explore and analyze data. Some of the most common EDA techniques include:

  • Descriptive Analysis:  Descriptive statistics serve the purpose of summarizing the features of a dataset. They include common statistics like the average (mean), middle value (median), most frequent value (mode), the spread of data (standard deviation), and the range of values.
  • Data Visualizations: Data visualizations are visual ways to represent data, and they are helpful for spotting patterns and trends. Examples of common data visualizations are histograms, bar graphs, line charts, scatter plots, and heatmaps.
  • Correlation Analysis: Correlation analysis is used to measure the strength and direction of the relationship between two variables.
  • Regression Analysis: Regression analysis is used to model the relationship between one or more independent variables and a dependent variable.
  • Clustering: Clustering is used to group similar data points together.
  • Dimensionality Reduction: It is a technique employed to trim down the number of attributes in a dataset while retaining the essential information it holds.

Benefits of Exploratory Data Analysis

There are several benefits of Exploratory Data Analysis in day-to-day analysis and performing data-oriented decisions in businesses. Here are the benefits of exploratory data analysis:

  • Data Quality Assurance: Exploratory Data Analysis (EDA) makes sure that the data we’re using for analysis is correct and dependable. It involves carefully looking at the data to find and fix mistakes, differences, or any missing parts. By doing this early on, EDA helps ensure that the results and insights we get from the data can be trusted. Correlation analysis is used to measure the strength and direction of the relationship between two variables.
  • Insights Discovery: Exploratory Data Analysis (EDA) acts as a data detective. It assists in unveiling concealed patterns, trends, and connections that may not be immediately obvious. Using visual representations and statistical examination, EDA brings to light valuable information that can enhance decision-making.
  • Anomaly Detection: Exploratory Data Analysis (EDA) is like a data guardian, watching out for unusual and abnormal data points that could mess up our analysis results. By catching these irregularities early on, EDA makes sure our data is clean and dependable, which stops us from drawing incorrect conclusions.
  • Feature Selection: In the world of data modeling, less can be more. EDA aids in selecting the most relevant variables (features) for your analysis. This reduces complexity, enhances model performance, and makes results more interpretable.
  • Improved Decision Making: Exploratory Data Analysis (EDA) equips businesses to make smart choices based on data. When organizations have a deep understanding of their data, they can confidently select strategies that have a higher chance of succeeding. EDA’s insights serve as a compass, guiding decision-makers toward the most effective routes to follow. 

Examples of Exploratory Data Analysis

To demonstrate how important EDA is in practice, let’s delve into some real-life instances where Exploratory Data Analysis has been of utmost significance:

  1. Exploring a Dataset to Predict Housing Prices

Imagine you’re given the task of creating a machine-learning model to forecast housing prices in a city. Before jumping into the complex world of model building, it’s crucial to conduct Exploratory Data Analysis (EDA). In this preliminary phase, you’d begin by collecting data on various factors such as house size, bedroom count, location, and past sale prices.

EDA serves as your guiding light, helping you comprehend the relationships among these variables and their impact on property prices. For instance, EDA might reveal that larger houses generally come with higher price tags. Additionally, it can spot unusual cases, like a small house with an unexpectedly high price, which might need further scrutiny or data adjustments.

Ultimately, EDA lays the foundation for critical decisions, including feature selection, model selection, and the application of validation techniques, ensuring a well-informed and effective approach to predicting housing prices.

  1.  Analyzing Customer Data to Improve Marketing Strategies

In the world of marketing, exploratory data analysis (EDA) can be a real game-changer. Imagine you work for an online store, and you want to sell more by creating special marketing campaigns for different types of customers. EDA helps by looking at customer info like age, what they’ve bought before, and how they use the website.

When you use EDA, it helps you group customers based on these things. For example, you might see which products are popular with different age groups or which ways of advertising work best for different types of customers. With these insights, you can make marketing plans that speak directly to specific groups of customers. This can boost sales and make customers really happy.


Exploratory Data Analysis (EDA) is an indispensable tool for data scientists to extract insights and make informed decisions. EDA will remain fundamental in the data analysis process, evolving to meet the demands of increasingly complex and large datasets. Automation, AI, real-time analysis, and ethical considerations will shape the future of EDA, making it an essential tool for data-driven decision-making.

To discuss more, visit our data science community!

Course Schedule

Name Date Details
Data Scientist Course 25 May 2024(Sat-Sun) Weekend Batch
View Details
Data Scientist Course 01 Jun 2024(Sat-Sun) Weekend Batch
View Details
Data Scientist Course 08 Jun 2024(Sat-Sun) Weekend Batch
View Details

About the Author

Principal Data Scientist

Meet Akash, a Principal Data Scientist who worked as a Supply Chain professional with expertise in demand planning, inventory management, and network optimization. With a master’s degree from IIT Kanpur, his areas of interest include machine learning and operations research.