Exploratory Data Analysis

Often, you might have to analyze and understand a lot of data in front of you for official or personal purposes. There are several different techniques and approaches you can take for understanding and interpreting it. The technique you choose would depend on the complexity of the technique and the kind of data that has to be analyzed. Here, we would be talking about discussing one of the simplest and most efficient methods of interpreting data – exploratory data analysis.

Exploratory Data Analysis: An Introduction

Exploratory Data Analysis is a statistical technique for analyzing and understanding data sets and summarizing their main characteristics. In most cases, the inferred data is represented using statistical graphics or other similar methods of data representation. Scientists, researchers, or academicians usually use the method of exploratory data analysis for seeing what the provided data says beyond the formal modeling.

The Exploratory Data Analysis or EDA method has been popularized by John Tuckey since 1970 as he encouraged data analysts to understand beyond just the face value of the data provided and formulate possible hypotheses for making the data collection process simpler and helping in further experimentation. The best part about the EDA approach is that it helps analysts answer the following questions regarding the data:

1. What are the data variables and how are they related?
2. What are the important variables that can help in solving the problem at hand?
3. What are the main features of the dataset present?

Using Explanatory Analysis for your Data

Using the EDA method may simplify data and give further insights into what may not seem important at first glance. After getting your dataset, you would have to glance through it and perform some checks and steps before it is ready for analysis. Follow the steps below to make your analysis accurate. 

1. Check for missing data: Sometimes, data may be missing. This may be due to an error on the recorder’s part, or simply because they felt mentioning the data was not worth it. In either case, the results would be far from accurate and would affect the company or organization greatly. So, looking for missing data and ranking them according to needs would be a great place to start. 
2. Categorize the sample data and describe them: After gathering the data, it is important to categorize it into continuous, discrete, or categorical data. You can also describe them, in brief, to make it easy for viewers to understand what the data pertains to. 
3. Identify the data distribution: The distribution shape of the data can help companies and organizations realize the possible shortcomings and strengths of their dataset.
4. Identifying possible variable relationships: This can help companies realize how the variables affect each other. 
5. Detect evident defects: Sometimes, some discrepancies may cause defects to pop up in a particular dataset. Even minor defects can have a dramatic impact on the accuracy of the exploratory data analysis, so it is better to avoid data that seems odd.

These steps would be very helpful in understanding how the explanatory analysis works for your data. You can look at the following exploratory data analysis example to get a better grasp of how the EDA method works.

Let us consider the dataset of every visitor in a particular shopping mall. The data available includes the age of the customers, most visited shops, most preferred food items and the time of visit. EDA method using a histogram would help understand the main age group of visitors, while a scatterplot of visitors vs time would help understand which age group of customers prefer what time of day. 

Exploratory Data Analysis in R Programming

Programmers and scientists often use the R programming language to perform exploratory data analysis for their data sets. Using exploratory data analysis in R can give two types of results- either a descriptive analysis or a graphical analysis. A descriptive analysis would give results like the mean, median, mode and interquartile range of the data set. On the other hand, graphical analysis focuses on the use of histograms, density estimation, box plots and the like for giving a result.

Conclusion

After reading this article, you must have certainly understood how the EDA method helps read and interpret datasets beyond their face value. You can use sample data sets available online and try out this method of data analysis to further your understanding. Trying out different data analysis methods would help you understand the subtle differences between them. Further, knowing their application would let you make a better decision when thinking of which method to use on a particular dataset.