Exploratory Data Analysis: Types, Charts and Examples

By PPCexpo Content Team

Exploratory data analysis (EDA) is an approach you can easily leverage to summarize the key attributes of your data.

Besides, it’s a methodology that employs graphs and charts to squeeze answers out of your raw data. This strategy uses data visualization designs and statistics to extract insights into data.

Visualizations or charts play a critical role in the business world by empowering data-backed decisions and providing insights for continuous improvement.

It would be best if you had easy-to-interpret charts and graphs to extract actionable insights from raw data. Also, you need the best tools to help you access ready-made charts.

Google Sheets is arguably among the most used data visualization tools because it has been around for years and is familiar to many.

But to conduct a comprehensive exploratory analysis using ready-made and visually appealing charts and graphs, you’ve got to think beyond the spreadsheet app.

Why?

Google Sheets produces very basic charts that require extra time and effort to edit.

However, you don’t have to do away with Google Sheets. You can transform it into a reliable data visualization tool by installing third-party applications (add-ons).

Table of Contents:

• What is Exploratory Data Analysis (EDA)?
• Why Exploratory Data Analysis (EDA) is Important?
• What are the Uses of Exploratory Data Analysis (EDA)?
• When to Use Exploratory Data Analysis?
• Types of Exploratory Data Analysis (EDA)?
• What Techniques are Used in Exploratory Data Analysis?
• Fundamentals of Exploratory Data Analysis
• Steps for Performing Exploratory Data Analysis
• How To Conduct Exploratory Data Analysis?
• Exploratory Data Analysis Examples
• Exploratory Data Analysis Tools
• FAQs
• Wrap Up

What is Exploratory Data Analysis (EDA)?

Definition: According to John Tukey (who coined the term exploratory analysis in the 1970s), exploratory analysis refers to the procedures and techniques for data analysis and interpreting the results.

Besides, it involves planning, tools, and statistics you can use to extract insights from raw data.

You can leverage EDA to explore what data can reveal beyond hypothesis testing. Besides, this strategy can help you to determine whether the statistical graphs you’re considering for analysis are appropriate.

Why Exploratory Data Analysis (EDA) is Important?

Until recently, making sense of bulky and complex raw data was too daunting for us.

But methodologies, such as exploratory analysis, are increasingly helping us to crunch gigantic data sets on an unprecedented scale.

The key advantage of the EDA methodology is that it allows you to investigate your raw data beyond hypothesis testing. You can leverage statistical models, such as averages, standard deviation, median, quartiles, etc., to probe your data for in-depth answers.

Remember, your database contains a wealth of information just waiting to be discovered. Even historical data collected from disparate sources makes more sense when visualized.

In today’s world, we generate tons of data every day. When leveraged fully, data can help your business to personalized marketing communication.

What are the Uses of Exploratory Data Analysis (EDA)?

You can leverage EDA for the following:

• You can use exploratory data analytics to check for missing data and other mistakes.
• Gain in-depth insights into the data sets and their underlying structure.
• Verify assumptions associated with the hypothesis tests.
• Check for outliers, patterns, and trends in your raw data.
• Find parameter estimates and their associated confidence intervals or margins of error.

When to Use Exploratory Data Analysis?

Exploratory Data Analysis (EDA) is best utilized at the beginning phases of information investigation to figure out the dataset’s construction, examples, and connections.

It’s especially valuable when you want to distinguish patterns, recognize peculiarities, or produce theories for additional examination.

EDA helps in cleaning the information, dealing with missing qualities, and choosing the right factors for demonstrating.

Use it when you’re uncertain about the information’s conveyance or need to reveal stowed-away bits of knowledge before applying further developed measurable or AI strategies.

This underlying investigation can direct your resulting examination and dynamic interaction.

Types of Exploratory Data Analysis (EDA)

There are 2 key variants of exploratory analysis, namely:

Univariate analysis and Multivariate Analysis. They could be graphical and non-graphical as well so as a whole they become four types.

• Univariate Analysis

This is the simplest form of EDA, which entails analyzing a single data point relative to dimensional variables for insights. The main purpose of the univariate analysis is to describe the data and find patterns that exist within it.

Examples of data visualization designs to use in this analysis are Simple Bar, Pie, Radial Bar Chart, etc.

• Multivariate Analysis

Multivariate analysis entails analyzing multiple variables for insights. The best charts to use for this analysis include a Scatter Plot, a Radar Chart, and a Double Axis Line and Bar Chart.

Check out the diagrams of the chart as shown below:

Scatter Plot visualization:

Source: chartexpo.com

Radar Chart Visualization:

Source: chartexpo.com

Double Axis Line and Bar Chart:

Source: chartexpo.com

What Techniques are Used in Exploratory Data Analysis?

Exploratory Analysis utilizes several techniques to understand the underlying patterns in data.

Common methods include summary statistics (mean, median, mode) to describe central tendency, data visualization (like histograms, box plots, and scatter plots) for identifying distributions and relationships, and data transformation (log transformations, normalization) to improve interpretability.

Correlation analysis helps uncover relationships between variables, while outlier detection highlights anomalies that may affect analysis. Together, these techniques provide a deeper understanding of data before applying formal modeling.

Fundamentals of Exploratory Data Analysis

Fundamentals of Exploratory Analysis include understanding, imagining, and summing up datasets to reveal examples, patterns, and oddities.

It incorporates utilizing strategies like rundown measurements, information representation (e.g., histograms, scatter plots), and relationship examination to acquire experiences.

EDA recognizes information quality issues, for example, missing or anomaly values, and helps with framing speculations for additional examination.

By investigating information from top to bottom, examiners can settle on informed conclusions about the most suitable models and methods for prescient examination.

By and large, EDA is a vital stage in transforming crude information into significant bits of knowledge.

Steps for Performing Exploratory Data Analysis

Steps for Performing Exploratory Data Analysis (EDA):

• Grasp Your Information: Begin by stacking the dataset and understanding its construction, information types, and introductory rundown insights.
• Information Cleaning: Handle missing qualities, eliminate copies, and right any irregularities in the information.
• Information Perception: Use diagrams like histograms, box plots, and dissipate plots to envision dispersions, connections, and examples.
• Include Designing: Make new factors or change existing ones to more readily catch experiences or address skewness.
• Factual Investigation: Perform relationship examination and speculation testing to distinguish huge examples and patterns.
• Statistical Analysis: Perform correlation analysis and hypothesis testing to identify significant patterns and trends.

How to Conduct Exploratory Data Analysis?

Google Sheets is among the popular go-to data visualization tools for professionals, business owners, and those exploring business research methods.

However, it lacks ready-to-use charts for EDA methodology in its library. In other words, you have to invest extra time and energy to edit charts to align with your data story.

Yes, you read that right.

You don’t have to waste time editing charts.

You have the option to supercharge your Google Sheets with third-party add-ons to access ready-made and EDA-friendly charts.

We recommend you download and install an add-on called ChartExpo in your Google Sheets.

So, what is ChartExpo?

ChartExpo is a super user-friendly add-on you can install in your Google Sheets to access ready-to-use and visually appealing visualizations for your exploratory analysis and Business Analytics needs.

This exploratory analysis-recommended tool also offers over 50 other ready-made and advanced charts to help you succeed.

How to install ChartExpo in Google Sheets?

• You can directly install the ChartExpo extension in Google Sheets from here.
• Once it is installed you can find it in the Google Sheets application in the top menu Extension and then find ChartExpo and then click Open.

• Once it is opened you will see the below screen and you can click on Create New Chart.

• You will find the list of available charts by ChartExpo.

• You can select any of your desired charts start visualizing your data and build your own data stories.

Exploratory Data Analysis Examples

In this section, we’ll cover the two main types of exploratory analysis, namely: univariate and multivariate analyses. You’ll also learn how to leverage ChartExpo to generate the best-suited charts associated with the main types of EDA.

Radar Chart

In this example, we’ll use the Radar Chart to visualize the tabular data below:

Products	Months	Number of Orders
Face Cream	Jan	80
Face Cream	Feb	99
Face Cream	Mar	93
Face Cream	April	80
Face Cream	May	70
Face Cream	June	65
Face Cream	July	85
Face Cream	Aug	90
Face Cream	Sep	80
Face Cream	Oct	75
Face Cream	Nov	65
Face Cream	Dec	80
Skin Lightening Cream	Jan	100
Skin Lightening Cream	Feb	60
Skin Lightening Cream	Mar	95
Skin Lightening Cream	April	75
Skin Lightening Cream	May	100
Skin Lightening Cream	June	60
Skin Lightening Cream	July	95
Skin Lightening Cream	Aug	75
Skin Lightening Cream	Sep	109
Skin Lightening Cream	Oct	80
Skin Lightening Cream	Nov	109
Skin Lightening Cream	Dec	75
Beauty Cream	Jan	50
Beauty Cream	Feb	55
Beauty Cream	Mar	51
Beauty Cream	April	40
Beauty Cream	May	45
Beauty Cream	June	30
Beauty Cream	July	39
Beauty Cream	Aug	45
Beauty Cream	Sep	56
Beauty Cream	Oct	39
Beauty Cream	Nov	48
Beauty Cream	Dec	44

• Copy and paste data into Google Sheets to get started with exploratory analysis charts.
• Type “Radar Charts” on the Search toolbar.

• Select the sheet holding your data.
• Fill in your metrics and dimensions.
• In our example, the key metric to fill in is the number of orders. Conversely, fill in the following variables in the dimension section: products and months.

• Complete visualizing data with Radar Chart by clicking the Create Chart button.

Insights

• The best-performing product is skin lightening cream because its best months eclipse face and beauty products.
• The worst-performing product is beauty cream.
• Face cream outperformed the skin-lightening cream during January, March, May, July, and November.

Pareto Chart

In this example, we’ll use the Pareto Chart to visualize the table below.

Products	Sales
Rouge	1579
Mascara	1962
Lipstick	3654
Foundation	2578
Powder	4942
Eyebrow pencil	5561
Eye shadows	2961
Nail polish	4831
Lip gloss	8961

• Transfer the data (above) to get started with the Pareto Chart.
• Type “Pareto Chart” on the Search toolbar.

• Fill in your metrics and dimensions. In our example, the key metric to fill in is Conversely, fill in the following variable in the dimension section: products.

• Complete the simple process by clicking the Create Chart button.

Insights

• Lip gloss, eyebrow pencil, powder, nail polish, and lipstick are the 20% of products driving the 80% sales of the brand.
• Lip gloss single-handedly accounts for 24% of the cumulative sales value.
• Eyebrow pencil accounts for 39% of the aggregate sales value.

In this section, we’ll use the Grouped Column Chart to analyze the data set below.

Let’s dive in.

Year	Internet Sales	Sales in Person	Sales via Phone
January	1036	345	691
February	456	263	526
March	741	400	666
April	561	913	211
May	361	864	464
June	801	210	425
July	342	278	786
August	456	1357	304
September	1674	581	550
October	647	245	144
November	298	567	201
December	457	421	222

• Transfer the data (above) to get started with the Grouped Column Chart.
• Type “Grouped Column Chart” on the Search toolbar.

• Fill in your metrics and dimensions. In our example, the key metrics to fill in are internet sales, sales in person, and sales via phone. Conversely, fill in the following variable in the dimension section:

• Click the Create Chart button to complete the simple process.

Insights

• The best-performing month was April, where sales in person outperformed the rest.
• In October, sales of phones remained the worst.
• Sales via the Internet recorded the best performance of the year during June.

Double Axis Line and Bar Chart

We’ll visualize the data set below using the Double Axis Line and Bar Chart.

Quartiles	Sales	Growth
Q1-19	7000	4.2
Q2-19	7606	7.6
Q3-19	7895	3.8
Q4-19	8242	4.4
Q1-20	8327	0.7
Q2-20	8768	5.3
Q3-20	9337	6.5
Q4-20	9589	2.7

• Export the table above into Google Sheets to get Double Axis Line and Bar Chart.
• Type “Double Axis Line and Bar Chart” on the Search toolbar.

• Fill in your metrics and dimensions.
• In our example, the key metrics to fill in are sales and growth. Conversely, fill in the following variable in the dimension section:

• Click the Create Chart button to finish the simple process.

Insights

• The best-performing quarter was the Q2 of 2019 because growth surpassed sales.
• On the other hand, the worst-performing period was Q1 of 2020.

Advantages of Exploratory Data Analysis

• The EDA methodology is flexible and can adapt to changes as the data analysis progresses.
• Also, it can provide a solid foundation for your analysis and storytelling tasks.

Applications of Exploratory Data Analysis (EDA)

• You can use the exploratory methodology to measure the central tendency, which can provide you with an overview of the univariate and multivariate variables.
• Central tendency is the measurement of mean, median, and mode.

Exploratory Data Analysis Tools

• Power BI: This Microsoft apparatus offers vigorous information perception abilities with intelligent outlines and charts. It is broadly utilized for business investigation and revealing, empowering clients to investigate information and uncover patterns.
• Excel: Despite being quite possibly of the most seasoned apparatus, Excel stays well known for speedy EDA assignments. It permits clients to perform information cleaning, turn tables, and make different diagrams for examination.
• ChartExpo: ChartExpo is a specific information perception device that works on the method involved with straightforwardly making progressed outlines from stages like Excel and Research Sheets. It offers extensive simple-to-utilize, outwardly engaging graphs, including Sankey outlines, Likert scales, and radar diagrams, without requiring progressed coding abilities. ChartExpo is intended to make information examination available to non-specialized clients by creating a natural simplified outline with worked-in customization choices.

FAQs:

What is exploratory data analysis used for?

Exploratory data analysis (EDA) is an approach to analyzing datasets to summarize their main characteristics, often with visual designs, such as tables, charts, and graphs. You can use the methodology to measure the central tendency (mean, median, mode, and range).

What is the purpose of EDA?

The primary goal of EDA is to help you unearth hidden insights into a data set. You can leverage the method to check for missing data and other errors. Also, you can use the methodology to extract in-depth insights into your data set and its underlying structure.

What are the two goals of exploratory data analysis?

There are two primary goals of exploratory analysis, namely:

• To check for missing variables and other errors that can distort key insights.
• To gain access to hidden insights, such as trends, outliers, and patterns in raw data.

Also, EDA is flexible and can adapt to changes as required.

Wrap Up

Exploratory analysis is a statistics-based methodology for analyzing data and interpreting the results. Besides, it involves planning, tools, and statistics you can use to extract insights from raw data.

There are 2 key variants of exploratory analysis, namely:

• Univariate analysis
• Multivariate analysis

The expert-recommended charts you can use to visualize different data include Pareto and Radar Charts, Scatter Plots, and a Double Axis Bar and Line Chart.

Using the EDA to extract insights from your raw data should never be overwhelming or complex, especially if Google Sheets is your primary tool.

Why?

Google Sheets does not come loaded with ready-made charts for EDA. You have to invest extra time and effort to work on the charts generated by the spreadsheet application.

We recommend you install third-party apps, such as ChartExpo in your Google Sheets to access ready-made, visually appealing, and EDA-recommended charts, such as Radar and Pareto Graphs.

ChartExpo is an add-on you can easily download and install in your Google Sheets.

Besides, it has over 50 more advanced and visually stunning charts to ensure you succeed in data storytelling. Unlike other tools, you don’t need programming or coding skills to visualize your data using ChartExpo.

Sign up for a 7-day free trial today to access visually appealing and ready-to-use charts.