Exploratory Data Analysis [EDA]: Techniques, Best Practices and Popular Applications

Increased user activity on the internet, sophisticated tools to monitor web traffic, the proliferation of smartphones, internet-enabled devices, and IoT sensors are the primary factors accelerating the rate of data generation in today's world.

In this digital age, organizations of all sizes realize that data can play a vital role in improving their efficiency, productivity, and decision-making capabilities, leading to increased sales, revenue, and profits.

These days, most companies have access to vast datasets, but merely having massive amounts of data doesn't add much value to the business unless enterprises analyze the information available and drive organizational growth.

Fortunately, the advent of powerful computer systems and innovative analytical technologies are enabling more and more businesses to analyze data effectively, empowering them to extract actionable insights from diverse data points. 

Today, big data analysis is no longer a subsidiary aspect for strategizing operational plans. Rather, it is increasingly gaining ground as one of the most crucial elements in optimizing the core business processes.

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According to the Modis survey, a whopping 97.44 percent of 347 companies believe that big data analytics is key to improving their organizational performance. Another study conducted by Tag Innovation School reveals that over 50 percent of 550 small and medium-sized enterprises surveyed expressed an interest in hiring data analysts. 

The growing adoption of data analytics is driving the demand for analytics professionals to a record high. Interestingly, the demand is not just limited to start-ups and IT giants. Skilled data analysts find takers across industry verticals, from media to sports, fashion to gaming, transportation, retail, and manufacturing.

The World Economic Forum predicts that by 2020, data analytics will become the most sought-after job worldwide. In a similar vein, IBM claims that the rising demand for data specialists will create 700,000 new employment opportunities by 2020. The U.S. Bureau of Labor Statistics reports that by 2026, there will be 11.5 million new recruitment.      

Notwithstanding the current economic downturn affected by the ongoing Covid-19 pandemic crisis, the above data presents an optimistic scenario for individuals seeking to pursue a career in the data analytics sector. In this article, we'll explore a holistic view of exploratory data analysis (EDA), ways to do exploratory data analysis, the techniques involved, and the skills you need to build or boost your career in data science with EDA skills. 

Here's how we can define data analysis:

Data analysis involves the process of cleansing, analyzing, transforming, combining, and modeling large datasets to uncover relevant, beneficial information that enables real-time scientific decision-making.

Data analysis features a wide range of approaches, facets, and techniques under different names, such as prescriptive analysis, predictive analysis, diagnostic analysis, statistical analysis, and text analysis.

In statistical applications, data analysis incorporates two key concepts - CDA (confirmatory data analysis) and EDA (exploratory data analysis). While CDA emphasizes on falsifying or confirming existing hypotheses, EDA zeroes in on exploring and identifying new data features. 

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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. 

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