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Home > Blog > Digital Marketing > Data Visualization >

What is Exploratory Data Analysis – EDA Types With Examples

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

Exploratory Data Analysis
Exploratory Data AnalysisExploratory Data Analysis

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.

To extract actionable insights from raw data, you need easy-to-interpret charts and graphs. 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 there for years and is familiar to many.

But to conduct a comprehensive exploratory data analysis (EDA) 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).

In this blog, you’ll learn:

  • How you can leverage exploratory data analysis (EDA) to create compelling data stories?
  • The best practices associated with EDA and statistics.
  • Also, you’ll learn about the best tool to use to conduct a comprehensive exploratory data analysis (EDA).

What is Exploratory Data Analysis (EDA)?

According to John Tukey (the person who coined the term exploratory data analysis in the 1970s), it’s the procedures and techniques for analyzing data 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 techniques you’re considering for analysis are appropriate.

Why Exploratory Data Analysis (EDA) is Important in Your Business?

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

But methodologies, such as exploratory data 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 the hypothesis testing. More so, you can leverage statistical models, such as averages, standard deviation, median, and quartiles, etc. to probe your data for in-depth answers.

Remember, there’s a wealth of information hiding in the data in your database just waiting to be discovered. Even historical data collected from disparate sources make 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 analysis 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.

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

There’re 2 key variants of exploratory data analysis, namely:

Univariate analysis and Multivariate Analysis. They could be graphical and non-graphical as well so as 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 etc.

Exploratory Data Analysis
Exploratory Data AnalysisExploratory Data Analysis

Visualization Source: ChartExpo

  • Multivariate Analysis

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

Check out the diagrams of the chart as shown below:

Scatter Plot visualization:

Exploratory Data Analysis

Visualization Source: ChartExpo

Exploratory Data AnalysisExploratory Data Analysis

Radar Chart Visualization:

Exploratory Data Analysis

Visualization Source: ChartExpo

Exploratory Data AnalysisExploratory Data Analysis

Double Axis Line and Bar Chart:

Exploratory Data Analysis

Visualization Source: ChartExpo

Exploratory Data AnalysisExploratory Data Analysis

How to Make Ready-Made and Insightful Charts for Exploratory Data Analysis?

Google Sheets is among the popular go-to data visualization tools for professionals and business owners.

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 an 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 data analysis (EDA).

Also, the exploratory data analysis-recommended tool has over 50 other ready-made and advanced charts to help you succeed.

How to install ChartExpo in Google Sheets?

You can directly install ChartExpo extension in Google Sheets from here.

Once it is installed you can find it in Google Sheets application in top menu Extension and then find ChartExpo and then click Open.

Exploratory Data Analysis

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

Exploratory Data Analysis

You will find list of available charts by ChartExpo.

Exploratory Data Analysis

You can select any of your desire chart and start visualizing your data and build your own data stories.

In the next section, we’ll cover exploratory data analysis examples to get you started with the easy-to-follow methodology.

Exploratory Data Analysis Examples

In this section, we’ll cover the two main types of exploratory data 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.

How to Make Different Charts with ChartExpo for 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 paste data into Google Sheets to get started with exploratory data analysis charts.
  • Type “Radar Charts” on the Search toolbar.
Exploratory Data Analysis
  • 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.
Exploratory Data Analysis
  • Complete visualizing data with Radar Chart by clicking the Create Chart button.
Exploratory Data Analysis
Exploratory Data AnalysisExploratory Data Analysis

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 the months of 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.
Exploratory Data Analysis
  • 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.
Exploratory Data Analysis
  • Complete the simple process by clicking the Create Chart button.
Exploratory Data Analysis
Exploratory Data AnalysisExploratory Data Analysis

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 (an exploratory data analysis-friendly visualization) 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 Grouped Column Chart.
  • Type “Grouped Column Chart” on the Search toolbar.
Exploratory Data Analysis
  • 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:
Exploratory Data Analysis
  • Click the Create Chart button to complete the simple process.
Exploratory Data Analysis
Exploratory Data AnalysisExploratory Data Analysis

Insights

  • The best-performing month was April, where sales in person outperformed the rest.
  • In October, sales on phone remained 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 (one of the exploratory data analysis examples).

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.
Exploratory Data Analysis
  • 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:
Exploratory Data Analysis
  • Click the Create Chart button to finish the simple process.
Exploratory Data Analysis
Exploratory Data AnalysisExploratory Data Analysis

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.

FAQs:

What is the exploratory data analysis?

Exploratory data analysis is a methodology in statistics you can use to investigate your raw data for patterns, trends, and anomalies. It involves planning, tools, statistics you can use to extract insights from raw data. You can leverage EDA to explore what data can reveal beyond hypothesis testing.

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 data analysis (EDA), namely:

  • To check for missing variables and other errors that can distort key insights.
  • To gain access into 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 data analysis (EDA) 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’re 2 key variants of exploratory data analysis, namely:

  • Univariate analysis
  • Multivariate analysis

The expert-recommended charts you can use to visualize different data include Pareto and Radar Charts, Scatter Plot 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 other 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.

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