What is Exploratory Data Analysis? Everything you need to know

Data scientists use exploratory data analysis (EDA) to investigate and analyze data sets and understand their main characteristics. They often rely on data visualization methods to spot patterns, identify anomalies, test hypotheses, and check assumptions. EDA is a great way to find out what data can reveal beyond formal modeling and hypothesis testing and get a better understanding of data variables and their relationships. It also helps to determine if the statistical techniques you’re planning to use for data analysis are appropriate. The EDA techniques were first developed by John Tukey, an American mathematician, in the 1970s and are still widely used today to discover new insights from data.

Why is Exploratory Data Analysis Important in Data Science?

The main purpose of EDA is to help check out data before assuming anything. It can help spot obvious mistakes and understand patterns in the data, find weird or unusual events, and discover interesting relations among variables.

Data scientists can use exploratory analysis to make sure their results are valid and can be applied to desired business outcomes and goals. EDA also helps stakeholders by confirming they are asking the right questions. EDA can help answer questions about standard deviations, categorical variables, and confidence intervals. Once EDA is done and insights are drawn, its features can be used for more advanced data analysis or modeling, including machine learning.

Exploratory Data Analysis Tools Functions 

The exploratory data analysis (EDA) tools offer multiple statistical functions and techniques that can assist in analyzing high-dimensional data containing several variables. These functions include:

• Clustering and dimension reduction techniques that help in creating graphical displays, 
• Univariate and bivariate visualizations with summary statistics to evaluate the correlation between variables
• Multivariate visualizations to map and comprehend interactions between different fields in the data.
• K-means Clustering, which assigns data points into K groups based on the distance from each group’s centroid. This method has many practical applications, such as market segmentation, pattern recognition, and image compression. 
• Predictive models, like linear regression, use statistical analysis and data to forecast outcomes.

Types of Exploratory Data Analysis

There are four primary types of EDA:

1: Univariate Non-graphical

Univariate analysis is a fundamental approach to data analysis, particularly when the data under consideration involves only a single variable. In such scenarios, exploring the causes or relationships among variables is not possible. Rather, the primary objective of univariate analysis is to describe the data and detect any existing patterns or trends. This technique serves as a crucial tool for understanding and interpreting data, enabling informed decision-making and strategic planning.

2: Univariate Graphical 

When examining data, the use of graphical methods is essential as non-graphical methods do not provide a complete picture. Univariate graphics are commonly used, including:

• Stem-and-leaf plots that display all data values and the distribution shape
• Histograms that represent the frequency or proportion of cases for a range of values
• Box plots that depict the five-number summary of minimum, first quartile, median, third quartile, and maximum.

3: Multivariate Nongraphical

Multivariate data often originates from more than one variable. Non-graphical exploratory data analysis (EDA) techniques for multivariate data generally reveal the relationship between two or more variables using cross-tabulation or statistics.

4: Multivariate Graphical

Multivariate data analysis is a statistical method that utilizes graphical representation to exhibit the correlation between two or more sets of data. The most commonly used graphical representation is a grouped bar plot or bar chart that depicts each group representing a single level of one variable, and each bar within a group denotes the levels of the other variable.

Other common multivariate graphics:

• Scatter plot: A visual tool that displays data points on a horizontal and vertical axis to demonstrate the relationship between two variables.
• Multivariate chart: A graphical representation that illustrates the correlation between factors and response.
• Run chart: A line graph that depicts data plotted over time.
• Bubble chart: A data visualization that shows multiple circles (bubbles) in a two-dimensional plot.
• Heat map: A  graphical representation of data where values are displayed through color.

Exploratory Data Analysis Tools

Some of the most common data science tools used to create an EDA include:

Python is an interpreted, object-oriented programming language that boasts dynamic semantics. Its high-level built-in data structures, coupled with dynamic typing and dynamic binding, make it highly attractive for rapid application development and use as a scripting or glue language that connects existing components. Python in conjunction with EDA can be utilized to identify missing values in a dataset, which is critical in determining how to handle missing values for machine learning.

R is an open-source programming language and free software environment that specializes in statistical computing and graphics, supported by the R Foundation for Statistical Computing. The R language is widely employed by statisticians in data science and is instrumental in developing statistical observations and data analysis.