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.
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.
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
Once you’ve identified the variables and have a better understanding of them, the next step is to clean up the dataset. This is important for many reasons and generally helps give a better understanding of the variables involved.
Usually, dataset cleaning starts by removing the redundant variables. For instance, in the used car example, you can remove redundant variables such as similar colors, such as “grey” or “cloudy grey” or different variations of a similar color.
Essentially, based on the conclusion you want to check for, you must get rid of any redundant variables. For instance, color shouldn’t be a viable variable if you want to understand the fuel economy of used cars.
Once you remove the redundant variables, the next step is to select variables that have an excess amount of null values. These are essentially useless variables that don’t yield any significant results. Once you’re done, the next step is to remove the outliers.
When defining the parameters for different factors, such as price, year, or the reading the odometer, you might want to identify ranges that shouldn’t be covered. For instance, there might be some cars that you don’t want to factor in, especially those that are older beyond a certain limit, or those which have been driven a certain amount.
Once you’re done with cleaning the dataset and removing any variables with null values, the next, and final step, is to understand and evaluate the relationship between each of the variables.
3) Evaluating the Relationship Between Variables
There are several ways to understand the relationship between different variables. For instance, a popular method is to make use of a correlation matrix. Correlation helps you understand how two variables are related to each other.
Ideally, you’ll want to determine if there is a positive correlation between two variables, or if there’s a negative one. Think about it this way, a positive correlation is likely to exist between the year and the price.
This means that if the car is relatively new, or not very old, it’s probably going to command a higher price in the market. Similarly, if a car is older, its price will be lower. On the other hand, a negative correlation is likely to exist between the reading on the odometer and the price of the vehicle.
The higher the odometer reading, the lesser the price. Another way to identify relationships between variables is to use scatterplots. Data visualization helps analysts gain a better understanding of different variables.
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 100+ Data Sources and helps you transfer your data from these sources to Data Warehouses in a matter of minutes, all without writing any code!
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Najam specializes in leveraging data analytics to provide deep insights and solutions. With over eight years of experience in the data industry, he brings a profound understanding of data integration and analysis to every piece of content he creates.
