What Is Exploratory Data Analysis?

Exploratory data analysis (EDA) is a common method used to validate data, generate hypotheses, and identify trends. Unlike traditional methods, which begin and end with a problem to solve, exploratory data analysis is open-ended and allows you to analyze and identify data trends. Explore what EDA is, how you can use it with different types of data, and which careers utilize this technique.

What is exploratory data analysis?

Exploratory data analysis (EDA) is an open-ended, iterative data analysis approach designed to unearth patterns, anomalies, relationships, or insights without preconceived notions. John Tukey, a renowned American mathematician, introduced EDA in the 1970s to analyze data using a combination of statistical tools and data discovery methods.

EDA contrasts with classical methods, which generally confirm a hypothesis. Instead, EDA is more like detective work. You don’t have an established idea about what the data might reveal, allowing you to generate hypotheses from the data sets themselves. This can drive informed decision-making and generate hypotheses that may benefit from additional research.

Read more: What Is Data Exploration?

What is the purpose of exploratory data analysis?

EDA has several purposes with applications across industries. For example, data analysts can use exploratory analysis to validate findings, while stakeholders can use EDA to determine which questions are most important to ask. 

In general, EDA is helpful for assessing the data objectively, describing it, and beginning to make sense of the findings before moving on to more complex statistical analyses. By performing EDA in the early stages after data collection, data analysts can more effectively assess data quality and fit the appropriate model without being limited by preconceived notions. This can maximize potential insights into the data structure and variable relationships.

When you perform EDA on a data set, you will likely have the following goals:

• Look for any irregular data points to reduce errors before analyzing

• Ensuring assumptions are met

• Explore data features and preliminary relationships between your variables

• Generate potential hypotheses from the data

• Identify which statistical methods are most appropriate for your data set

Types of exploratory data analysis

EDA techniques can be broadly divided into four types, depending on the kind of analysis and the number of variables involved. Here are the following:

1. Univariate non-graphical

Univariate non-graphical EDA involves the analysis of a single variable using statistical techniques. You would choose this approach if you wanted to summarize a single variable’s data distribution using statistical measures like central tendency (mean, median, mode), spread (range, variance, standard deviation), or distribution (skewness, outliers).

2. Multivariate non-graphical

While univariate analysis looks at one variable, multivariate non-graphical EDA involves examining multiple variables simultaneously. Cross-tabulation, covariance, and correlation are measures that are commonly used to look at how several variables relate to each other. These variables may be outcome variables, exposure variables, or a combination of both. 

Cross-tabulation is generally chosen when you have a low number of variables. For example, if you had two variables, you could construct a table with your column headings representing levels of one variable and your row headings representing levels of another. You could then insert the number of data points that share each pair of levels. This can provide a general insight into how the two variables might relate to one another.

Correlation and covariance show the strength and direction of the relationship between different variables. For example, if you have a positive covariance measure for two variables, it would mean they move in the same direction as one another. A negative covariance measure would mean they move in opposite directions as one another.

3. Univariate graphical

Univariate graphical EDA uses visualization techniques to understand and interpret a single variable. While non-graphic methods can give objective summarization of the data, plots like histograms, box plots, quantile-normal plots, and stem-and-leaf plots can provide helpful visualization of the data. This visualization can give insights into the distribution of a variable, including its central tendency, dispersion, and the presence of outliers.

4. Multivariate graphical

Multivariate graphical EDA involves the simultaneous graphical analysis of multiple variables. Multivariate graphical EDA is most commonly used when you have two categorical random variables, for which you can create a grouped bar plot. In a grouped bar plot, you would have each group represent one level of one variable, and bars within each group represent levels of additional variables.

You could also choose to showcase multivariate graphical representations of your data using scatterplots, bubble charts, heat maps, or multivariate charts.

Exploratory data analysis real-world examples 

EDA has applications across sectors and can benefit professionals in any industry who want to generate hypotheses and find natural patterns in data sets. 

• You will often see this method used in education, where large volumes of data are constantly collected to help educators and policymakers make decisions and best use their resources. Education professionals can use EDA with metrics such as achievement scores, poverty data, demographic factors, and program evaluation to predict future impacts from program implementation or institutional changes. This type of analysis helps to drive necessary changes and identify areas of improvement outside of what industry professionals anticipated.

• Public health is another field that benefits from exploratory data analysis. In one research study, scientists wanted to assess how effectively they could monitor people’s health using a remote health care monitoring system. To do this, researchers gathered data on biological measures such as heart rate, body temperature, and pulse oxygen saturation level using cell phones and other mobile technology. Researchers were able to use exploratory analysis to assess participant activity during the study period and use trends found in this data to validate their monitoring methods and inform future health care monitoring efforts [1].

Careers in exploratory data analysis

Several data-driven careers use exploratory data analysis. Some common ones include data scientists, data analysts, and machine learning scientists. 

Read more: 4 Data Analyst Career Paths: Your Guide to Leveling Up

1. Data scientist

Average annual US salary (Glassdoor: $112,184 [2]

Job outlook (projected growth from 2022 to 2032): 35 percent [3]

Education requirements: To become a data scientist, you will likely need to earn a bachelor’s degree, often in math, statistics, or computer science. 

Data scientists use EDA to investigate complex data sets and extract valuable insights. These insights can drive strategic decisions and influence business outcomes. EDA enables data scientists to have a comprehensive understanding of data, which is vital in creating accurate predictive models and algorithms.

Read more: What Is a Data Scientist? Salary, Skills, and How to Become One

2. Data analyst 

Average annual US salary (Glassdoor): $83,513 [4]

Job outlook (projected growth from 2022 to 2032): 35 percent [3]

Education requirements: To become a data analyst, you will most likely need to earn a bachelor’s degree, often in computer science, statistics, or a related field. 

Exploratory data analysis is also critical in the data analytics lifecycle. Data analysts must understand how to appropriately organize, manage, and interpret data so they can achieve the best outcomes for their clients. Within this lifecycle, EDA allows data analysts to test the data and begin to find answers to the objectives of the analysis. They can fit different types of statistical models and determine the best way to achieve their desired goals.

Read more: What Does a Data Analyst Do? Your Career Guide

3. Machine learning scientist

Average annual US salary (Glassdoor): $120,509 [5]

Job outlook (projected growth from 2022 to 2032): 23 percent [6]

Education requirements: To become a machine learning scientist, you will likely need to earn a bachelor’s degree, commonly in data science, computer science, or math. 

Machine learning scientists build algorithms that allow machines to learn from data, adapt to new information, make predictions, and analyze high volumes of information. EDA is a crucial step in their workflow and is often an early step in the data exploration process. EDA helps machine learning scientists understand the data’s structure, identify important variables, and uncover any underlying patterns or correlations that can be modeled.

Read more: What Is a Machine Learning Engineer? (+ How to Get Started)

How to become a data analyst 

A few things you can do to pursue a data analyst career path include the following:

• Take courses or complete a degree: Although not always a strict requirement, a bachelor’s degree in fields like mathematics, statistics, economics, or computer science can provide a strong foundation for a career in data analysis.

• Learn programming languages: Proficiency in programming languages, such as Python and R, widely used in data analysis, is a necessary step to learning EDA. These languages have powerful libraries for data manipulation, statistical analysis, and data visualization.

• Get familiar with data visualization tools: Visualization is a key component of EDA. Tools, such as Tableau and PowerBI, or libraries, such as Matplotlib and Seaborn in Python, are commonly used to create compelling data visualizations.

Read more: 5 Types of Data Visualization

Getting started with Coursera

Exploratory data analysis is an open-ended way of interacting with data to determine methodology and identify important variables, as well as building and assessing models. Although it’s open-ended, you can still apply a number of techniques depending on the data set you are examining. When considering a career that uses EDA, you might consider becoming a data scientist, data analyst, or machine learning scientist. 

You can expand your knowledge of data analytics methods with top-rated courses, Specializations, and Professional Certificates on Coursera. Consider completing the Google Data Analytics Professional Certificate for a comprehensive overview of entry-level data analytics methods. You will have the opportunity to build job-ready skills in less than six months through a structured lecture series taught by industry professionals.