Exploratory Data Analysis: Techniques, Best Practices & Applications

EDA Basics

Data scientists implement exploratory data analysis tools and techniques to investigate, analyze, and summarize the main characteristics of datasets, often utilizing data visualization methodologies. 

EDA techniques allow for effective manipulation of data sources, enabling data scientists to find the answers they need by discovering data patterns, spotting anomalies, checking assumptions, or testing a hypothesis. 

Data specialists primarily use exploratory data analysis to discern what datasets can reveal further beyond formal modeling of data or hypothesis testing tasks. This enables them to gain in-depth knowledge of the variables in datasets and their relationships.  

Exploratory data analysis can help detect obvious errors, identify outliers in datasets, understand relationships, unearth important factors, find patterns within data, and provide new insights. 

Developed in the 1970s by American statistician John Tukey - famed for his box plot techniques and the Fast Fourier Transform algorithm - EDA continues to find relevance even today in the field of statistical analysis. It allows data professionals to produce relevant and valid results that drive desired business goals.

Exploratory Data Analysis Examples

Clinical Trial

The open-access, peer-reviewed scientific journal PLoS ONE published a clinical group study in which researchers used exploratory data analysis to identify outliers in the patient population and verify their homogeneity. 

The scientists classified the patients participating in the study into forty attributes, including age and gender. EDA helped them determine that female groups in the study were more homogeneous than their male counterparts. This prompted the researchers to conduct separate medical tests for the male groups to avoid false findings in the clinical trial.

Retail

For example, an online store sells various types of footwear, such as sandals, sneakers, dress shoes, hiking boots, and formal shoes. 

Exploratory data analysis can enable analysts to represent different sales trends graphically and visualize data related to best-selling product categories, buyer demographics and preferences, customer spending patterns, and units sold over a certain period.  

Without EDA, this would not have been possible.

How to Perform Exploratory Data Analysis?

Data specialists perform exploratory data analysis using popular scripting languages for statistics, such as Python and R. For effective EDA, data professionals also use a variety of BI (Business Intelligence) tools, including Qlik Sense, IBM Cognos, and Tableau.  

Python and R programming languages enable analysts to analyze data better and manipulate it using libraries and packages such as Plotly, Seaborn, or Matplotlib.

BI tools, incorporating interactive dashboards, robust security, and advanced visualization features, provide data processors with a comprehensive view of data that helps them develop Machine Learning (ML) models.

The exploratory data analysis steps that analysts have in mind when performing EDA include:

• Asking the right questions related to the purpose of data analysis
• Obtaining in-depth knowledge about problem domains
• Setting clear objectives that are aligned with the desired outcomes.

Exploratory Data Analysis Techniques

There are four exploratory data analysis techniques that data experts use, which include:

Univariate Non-Graphical

This is the simplest type of EDA, where data has a single variable. Since there is only one variable, data professionals do not have to deal with relationships. 

Univariate Graphical

Non-graphical techniques do not present the complete picture of data. Therefore, for comprehensive EDA, data specialists implement graphical methods, such as stem-and-leaf plots, box plots, and histograms. 

Multivariate Non-Graphical

Multivariate data consists of several variables. Non-graphic multivariate EDA methods illustrate relationships between 2 or more data variables using statistics or cross-tabulation.

Multivariate Graphical

This EDA technique makes use of graphics to show relationships between 2 or more datasets. The widely-used multivariate graphics include bar chart, bar plot, heat map, bubble chart, run chart, multivariate chart, and scatter plot. 

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The Way Forward With EDA

Exploratory Data Analysis is evidently one of the most important steps during the entire process of extracting insights out of data, even before the actual analysis or modeling begins. Therefore, for organizations that want to truly harness the power of data, putting their strengths and focus on the EDA phase could help them set up a solid foundation for their overall analysis efforts. In order to excel at this stage, they need to hire data professionals skilled in visualization, pattern recognition, creating maps, and other basic concepts of exploratory analysis besides key data analyst skills.

To develop vital skills related to tools, methods, techniques, and the commonly used programming languages for exploratory data analysis, aspiring data analysts should consider pursuing a comprehensive program in data analytics from a world-class educational institution such as Simplilearn.

Simplilearn's Post Graduate Program in Data Analytics, in collaboration with Purdue University and IBM, offers access to 14+ hands-on projects on integrated labs, 180+ hours of Blended-Learning, masterclasses from Purdue faculty, Simplilearn's JobAssist program, and a Purdue Alumni Association Membership. Click here to get an industry-recognized IBM certificate within 8 months.