Learn everything you need to know about exploratory data analysis, a method used to analyze and summarize data sets.
Exploratory data analysis (EDA) is used by data scientists to analyze and investigate data sets and summarize their main characteristics, often employing data visualization methods. It helps determine how best to manipulate data sources to get the answers you need, making it easier for data scientists to discover patterns, spot anomalies, test a hypothesis, or check assumptions.
EDA is primarily used to see what data can reveal beyond the formal modeling or hypothesis testing task and provides a provides a better understanding of data set variables and the relationships between them. It can also help determine if the statistical techniques you are considering for data analysis are appropriate. Originally developed by American mathematician John Tukey in the 1970s, EDA techniques continue to be a widely used method in the data discovery process today.
The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, find interesting relations among the variables.
Data scientists can use exploratory analysis to ensure the results they produce are valid and applicable to any desired business outcomes and goals. EDA also helps stakeholders by confirming they are asking the right questions. EDA can help answer questions about standard deviations, categorical variables, and confidence intervals. Once EDA is complete and insights are drawn, its features can then be used for more sophisticated data analysis or modeling, including machine learning.
Specific statistical functions and techniques you can perform with EDA tools include:
There are four primary types of EDA:
Other common types of multivariate graphics include:
Some of the most common data science tools used to create an EDA include:
For a deep dive into the differences between these approaches, check out "Python vs. R: What's the Difference?"
Use IBM Watson® Studio to determine whether the statistical techniques that you are considering for data analysis are appropriate.
IBM Watson® Studio provides an interface for analysts and data scientists to dig deeper into their datasets. This helps them to provide summary insights to their stakeholders and it also allows them to evaluate if the datasets are balanced enough to build meaningful models.