Exploratory Data Analysis

Exploratory Data Analysis, which is one of the most important ways to comprehend data, has evolved dramatically over the past few years. The growth has been fueled partly by the rise of complicated tools and sophisticated techniques. 

But, what is Exploratory Data Analysis? And, more importantly, why is it so important? In the following article, we are going to go over the basics of Exploratory Data Analysis and determine its importance.

What is Exploratory Data Analysis

The idea of Exploratory Data Analysis, or EDA as it’s commonly known, is not new. It was first pitched by James Wilder Tukey in 1977. And, while there are still quite a few commonalities with the way data analysis is still conducted, the philosophy has changed dramatically. 

EDA primarily begins with an objective or a business goal. Then, analysts work to find appropriate conclusions that support the business goal, primarily depending on the reviewed data. 

Analysts use a variety of different techniques to gain a better understanding of the dataset they’re evaluating. It’s important to understand the dataset. However, that could mean several things for a data analysts: 

• Identifying key variables and leaving useless ones
• Identifying any abnormalities, outliers, or instances of error
• Gaining a better understanding of the relationship (or lack of) between different variables 

Types of Data Analysis

There are several types of data analyses that you should know about. In the following paragraphs, we are going to talk about univariate, bivariate, and multivariate analysis. 

• Univariate Analysis
• Bivariate Analysis
• Multivariate Analysis

1) Univariate Analysis

Univariate analysis is the simplest form of data analysis. This includes analyzing data that has only one variable. It’s commonly used to measure the central tendency, such as mean, mode, or median. 

Histograms are the most commonly used data visualization technique for conducting a univariate analysis.

2) Bivariate Analysis

As the name suggests, this includes analyzing the relationship between two variables. These variables could either be mutually exclusive or might be dependent on one another. 

Scatter plots are mainly used to determine the relationship between two variables, with one variable being charted on the x-axis and the other one on the y-axis.

3) Multivariate Analysis

The multivariate analysis involves analyzing the relationship between three or more variables. If there are multiple variables to consider (a trivariate analysis includes three variables), a 3D model can be created to analyze the relationship between each variable. 

Key Components in Exploratory Data Analysis

There are several key components involved in EDA. Let’s go through them one by one. 

• Understanding Variables
• Cleaning the Dataset
• Evaluating the Relationship Between Variables

1) Understanding Variables

Almost all datasets have variables. When exploring different datasets, it’s important to first identify the variables. This can help analysts gain a better understanding of how conclusions might change based on the input variables. 

For instance, if you’re comparing a dataset that includes a bunch of used cars, the variables might include the shape, size, make, or model of the car. This, in turn, may affect fuel economy, which is a crucial insight. 

Before starting with Exploratory Data Analysis, analysts need to gain a better understanding of each. For instance, you can get the standard deviation, mean, median, and mode by running simple commands. This can help you better understand how the performance varies throughout the dataset. 

Now, considering the same used car dataset, there’s also the chance that many different descriptors might be used. These include words like “excellent,” “like new,” “barely driven,” and others. In some cases, it does make sense to group them when identifying different variables. 

2) Cleaning the Dataset

Cleaning a dataset involves several key steps to ensure relevance and accuracy for analysis. First, remove redundant variables, for example, group similar values such as “grey” and “cloudy grey” into one category. Next, remove irrelevant variables based on the purpose of the analysis; for example, “color” is irrelevant when evaluating fuel economy in used cars. 

Finally, remove variables with excessive null values since they do not add much value. Identify and eliminate outliers by setting acceptable ranges for factors such as price or mileage, which exclude data that falls outside the boundaries of these parameters. Lastly, analyze how relationships exist between the cleaned variables to reveal important patterns.

3) Evaluating the Relationship Between Variables

Methods like a correlation matrix are typically used to understand the relationships between variables. Correlation identifies whether the variables are positively or negatively related. For instance, a positive correlation may be observed between the year of a car and its price, whereby newer cars generally have a higher price. Conversely, a negative correlation may be found between odometer readings and price because higher mileage normally reduces the value of the car.

Another effective method is scatterplots, allowing analysts to visualize the relationships and patterns that exist between variables, thus enhancing their insights through data visualization.

Advantages of Using Exploratory Data Analysis

There are several reasons for using Exploratory Data Analysis today. Here are a few:

• EDA greatly improves an analyst’s core understanding of different variables. They can extract different pieces of information, including averages, mean, minima and maxima, and other relevant points.
• More importantly, EDA can help analysts identify major errors, any anomalies, or missing values in their dataset. This is important before a comprehensive analysis begins, and can help organizations save a great deal of time. 
• EDA can also help analysts identify key patterns. They can easily visualize the data through different types of graphs, ranging from box plots to histograms. 

Arguably the biggest benefit of using EDA is that it allows organizations to understand their data in a much better manner, and to use the tools available to them more effectively to derive key insights and conclusions. 

Conclusion

If you’re looking to conduct Exploratory Data Analysis, you must consider saving your data in a centralized data warehouse. One of the best ways to do this is by using Hevo.

To become more efficient in handling your Databases, it is preferable to integrate them with a solution that can carry out Data Integration and Management procedures for you without much ado and that is where Hevo Data, a Cloud-based ETL Tool, comes in. Hevo Data supports 150+ Data Sources and helps you transfer your data from these sources to Data Warehouses in a matter of minutes, all without writing any code!

Try a 14-day free trial and experience the feature-rich Hevo suite firsthand. Also, check out our unbeatable pricing to choose the best plan for your organization.

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