Over the past few years, data has evolved as the new oil. This means, like oil, data has become so valuable that businesses are leveraging it to make better and more informed decisions.
In this internet age, each day witnesses the generation of quintillion bytes of data. However, this data is in a raw format, i.e., it is not in usable form. Such unrefined data is of no use for businesses. As a result, there rises a need to refine raw data into a format so that it becomes useful. This is where data cleaning comes into the picture.
We collect data from multiple sources; hence, there are significant chances of data duplication. Also, the collected data may contain incorrect and incomplete data. Data cleaning entails identifying and removing all incorrect, corrupted, and incomplete data, along with one that is incorrectly formatted, from data sets , tables, or databases .
Before commencing data analysis or using data for machine learning algorithms , data has to be correct and in a suitable form. In addition to this, you must know any significant correlations and recurring patterns present in your data. In order to accomplish this, exploratory data analysis comes in handy.
Exploratory data analysis is a kind of data analytics. It is an approach to analyzing data sets and leverages statistical graphics and other data visualization techniques to summarize the characteristics of data sets. It plays a crucial role in dealing with data.
This blog post aims at explaining what exactly an exploratory data analysis is and its various aspects.
So, let us get started!
What is Exploratory Data Analysis (EDA)?
Often abbreviated as EDA, exploratory data analysis is the process of analyzing data sets in-depth and uncovering different characteristics or insights by leveraging different visualization techniques. Alternatively, it is a philosophy that employs a wide variety of visualization techniques to:
- Gain in-depth insights into a data set.
- Identify underlying structure.
- Extract significant variables.
- Identify outliers and anomalies.
- Test underlying assumptions.
- Develop parsimonious models.
- Decide optimal factor settings.
It seems pretty tedious and intimidating when analyzing data by looking at columns or a whole spreadsheet to identify and uncover the characteristics of data. It becomes overwhelming to derive insights by looking at plain numbers in spreadsheets. The best possible solution to this issue is exploratory data analysis (EDA), which leverages visual techniques.
EDA enables you to know your data better and extract useful hidden patterns and trends from it, even before the modeling of data takes place. It helps you have a better grasp of data set variables and the relationship between them.
In addition, this approach assists you in determining whether the statistical techniques you leverage in data analysis are relevant or not. It aids in altering data sources so that it becomes easy for data scientists to uncover patterns and trends, check assumptions, test a hypothesis, and identify and locate anomalies.
Development of EDA
In the 1970s, John W. Tukey introduced the term exploratory data analysis. In addition, he published a book with the same name in 1977. In his book, he stated that statistical hypothesis testing is given a lot of importance and attention; instead, it is important to emphasize using data to create testable hypotheses. However, he stated that employing both types of analyzes on the same data set could result in systematic bias because of the problems present in the testing hypotheses raised by data.
The following are the key objectives of EDA:
- Permits unexpected discoveries in data.
- Validates assumptions that form the basis for statistical inference.
- Assist in selecting appropriate statistical tools and approaches.
- Offers a foundation for further collection of data via surveys or experiments.
Importance of Exploratory Data Analysis in Data Science
As previously noted, EDA enables us to comprehend data much more thoroughly before drawing any conclusions. It also helps in spotting obvious mistakes, comprehending data variables and how they relate to one another, recognizing data trends, and spotting unusual events.
As a result, data scientists use EDA to ensure that the conclusions of their analysis are reliable and applicable to all business outcomes and objectives. The exploratory analysis provides answers to questions on various aspects, including standard deviations, confidence intervals, and categorical variables.
The insights drawn out of EDA are employed for performing sophisticated data analysis or modeling, along with machine learning .
Types of Exploratory Data Analysis
The most basic types of exploratory data analysis are Univariate and Multivariate. Further, each type has two sub-types, namely graphical and non-graphical. Let us discuss the types of exploratory data analysis below.
1. Non-Graphical Univariate EDA
The term ‘uni’ implies single, and ‘variate’ refers to a variable. As its name suggests, univariate EDA is associated with a single variable data. This means that the data under analysis comprises a single variable, which refers to one specific feature. Hence, this type of EDA does not deal with the causes or relationships of data variables.
The foremost objective of Univariate EDA is to explain data and extract trends or patterns from it. As it is a non-graphical type, it does not give the full picture of the data.
2. Graphical Univariate EDA
In contrast to non-graphical univariate EDA, graphical univariate EDA leverages graphical methods to describe single-variable data. Both graphical and non-graphical univariate EDAs are the same, but the representation approach is different.
Here are some popular graphical methods used in Univariate EDA:
Box plots, also known as whisker plots, leverage a five-number summary to depict data. The five-number summary is minimum, first quartile, median, third quartile, and maximum.
A histogram graphically represents data in the form of rectangular bars, where each bar represents the frequency or proportion (count/total count) of data items in successive numerical intervals of fixed size.
The stem-and-leaf plot graphically displays quantitative data, analogous to the histogram, to visualize the shape of the distribution. It splits a data value into two parts: stem (first digit(s)) and leaf (last digit).
From the above diagram, we can understand how the data is represented in the stem-and-leaf plot.
3. Non-Graphical Multivariate EDA
Unlike univariate EDA, multivariate EDA entails the analysis of data that forms by combining multiple variables. Non-graphical multivariate EDA utilizes cross-tabulation or statistics to determine the relationship between different variables of data.
4. Graphical Multivariate EDA
Graphical multivariate EDA entails the use of graphical techniques to represent the relationship between two or more variables of data. When it compares two variables simultaneously, it is a bivariate EDA, whereas comparing more than two variables at a time refers to a multivariate EDA.
The following are different graphical techniques in multivariate EDA:
When you need to describe the relationship between two variables, a scatter plot comes in handy. It entails plotting data points on a horizontal and vertical axis and determining how one variable affects the other. Each dot’s position on the horizontal and vertical axis represents the values of an individual data point. It also identifies patterns when the data is taken as a whole.
A run chart is a simple data visualization technique that displays collected data in a line graph that is plotted over time. It helps you detect trends and patterns in data and oversee data over time.
A heat map or heatmap is yet another data visualization technique that entails the representation of data, where values are depicted using a color-coding system. It immediately provides a visual summary of information.
In the above image, the blue color represents Republican states, while the red color represents Democratic states.
Tools used for Exploratory Data Analysis
Here are the two common tools to create EDA:
R is both a programming language and a software environment for statistical computing and graphics. Statisticians widely use the R language for statistical computing. The software environment is free and open-source and runs on major operating systems. It provides features such as data manipulation, graphical display, and data calculation.
It offers a plethora of statistical techniques, including linear and non-linear modeling, classification, clustering, time-series analysis, classical statistical tests, etc., along with graphical techniques. You can leverage mathematical symbols and formulae in R to create high-quality and well-designed plots.
Python is a general-purpose, interpreted, high-level language. It has emerged as the most preferred language among data professionals. The language comes with dynamic semantics, built-in data structures, dynamic binding, and dynamic typing. These features of Python make it a great choice for rapid application development (RAD).
EDA and Python go well hand in hand to detect missing values in data sets. Such spotting of missing values plays a vital role in machine learning.
Functions to Perform Using EDA Tools
The following is a list of functions or techniques that you can perform using EDA tools:
- Clustering and dimension reduction techniques: They help you create a graphical representation of high-dimensional data that comprises multiple variables.
- K-means clustering technique : It is a type of unsupervised machine learning . This technique involves assigning data points to different clusters or k-groups based on the distance of each cluster’s centroid. It is popularly employed in market segmentation, image compression, and pattern recognition.
- Univariate, bivariate, and multivariate data visualization: EDA plays a vital role in all three types of data visualization for summary statistics, identifying the relationship between each variable in a data set, and understanding the interaction of fields in data.
Here ends our discussion on exploratory data analysis (EDA). It is a significant approach to analyzing data and uncovering its characteristics or different patterns and trends from it. This technique helps you comprehend your data to its fullest and understand the relationship between different data variables.
We hope you found this article insightful and easy to understand the concept of EDA.
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