Imagine you and your friends decide to hang out and visit a place that you have never heard of before. In such a situation, you will find yourself in a puzzling state in order to make the final decision about whether to visit the place or not. Therefore, in order to arrive at a final decision, you will ask a few questions, such as where is the exact location? Is it safe to go there? And to get a preview, you would also search for the location online. Ultimately, after taking all the investigating measures, you and your friends finally go and visit that place. And this is what data scientists in their language call Exploratory Data Analysis (EDA). In this article, let’s understand what is exploratory data analysis and the basics of exploratory data analysis.
What Is EDA?
Data scientists use exploratory data analysis (EDA) to investigate and analyze data sets by employing data visualization methods and summarizing the main characteristics. EDA helps data scientists to manipulate data sources to get the answers they need, and as a result making the data analysis process easy for discovering patterns, testing a hypothesis, spotting anomalies, or checking assumptions.
Therefore, EDA is primarily used to reveal a better understanding of data set variables and whether the data scientists’ statistical techniques are appropriate. Originally, EDA was developed by an American mathematician John Tukey in the 1970s.
Why Is Exploratory Data Analysis Important?
EDA is important as it allows data scientists to analyze the data before coming to any assumptions and ensures that the results produced are valid and applicable to business outcomes and goals.
It has the following features:
- Helps identify errors
- Promotes better understanding of patterns within the data
- Helps detect abnormal events.
- Helps understand data set variables and the relationship among them.
Moreover, exploratory data analysis can help answer questions related to standard deviations, categorical variables, and confidence intervals.
If you are interested in learning more about Exploratory Data Analysis, check out the new blog related to Objectives of Exploratory Data Analysis which defines the milestones in your data science journey.
Tools and Types of EDA
You can check out the EDA tools and types of exploratory data analysis that data scientists use.
EDA Tools
The commonly used EDA tools are as follows:
- R
It is an open-source programming language. so, This programming language provides a free software environment for statistical computing and graphics. Data scientists or other statisticians commonly use the R language to develop statistical observations and data analysis.
- Python
It is an interpreted, object-oriented programming language with dynamic binding. hence, It allows data scientists to spot missing values of the data set. Since analyzing a dataset is a time-consuming process, Python offers open-source modules that help automate the entire process of EDA to save time and effort. A python is an excellent tool for EDA as it offers high-level, built-in data structure, dynamic typing, and binding.
- Excel
It is the simplest tool to start your data exploration. With many built-in functions and add-on tools, we can perform in-depth analysis.
With the help of EDA tools described above, EDA can also perform the following statistical functions and techniques:
- Perform K-Means clustering, which is also a popular clustering method in unsupervised learning where data points are assigned into clusters or K-groups. This kind of clustering method is commonly used in pattern recognition, market segmentation, and image compression.
- EDA is use in Predictive Models such as linear regression to predict outcomes.
Types of Exploratory Data Analysis
There are four types of EDA, they are as follows:
- Univariate Non-Graphical: This is also the simplest type of EDA among the other options. The univariate non-graphical analysis consists of only a single variable. The main objective of this type of EDA is to describe the data and find patterns within it.
- Univariate Graphical : Unlike the previous type of EDA, as the name suggests, this method provides a graphical display of the data. It involves different kinds of analysis methods, including histogram, box plots, and stem and leaf plots.
- Multivariate Non-Graphical: The multivariate non-graphical type of EDA consists of multiple variables and establishes relationships between variables using cross-tabulation or statistics.
- Multivariate Graphical: In this type of EDA, graphics display the relationship among two or more data sets. Bar charts and scatter plots are the most used charts under this category.
If you would like to read our article on How To Perform Exploratory Data Analysis, here is the link.
Exploratory Data Analysis with Techcanvass
We hope that this article gave you an idea about the what, why, and how of EDA. Stay tuned for more such informative articles where we practice the different EDA Methods on datasets.
Conclusion
Exploratory Data Analysis is an integral approach towards data analysis in order to drive valid assumptions and data results. So, this article covers the basics of exploratory data analysis to give you an idea about how data professionals utilize EDA in their day-to-day tasks. If you liked the article, let us know in the comments below.
You can also find out more about Exploratory Data Analysis in Visualization, visit our blogs to access more articles.
