In this article, we’ll discuss Exploratory Data Analysis (EDA) and its purpose in helping us understand our data.
The purpose of EDA is to help us gain insights into our data and identify patterns, relationships, and outliers that might not be immediately apparent. Think of it like being a detective and trying to uncover the truth about your data.
Data exploration is different from looking at individual data values because it involves looking at the data as a whole and trying to find patterns and relationships between different variables. It’s like trying to put together a puzzle, where each piece of data is a piece of the puzzle that helps you see the bigger picture.
EDA helps us get familiar with the data. It shows us how to clean the data by identifying missing values, outliers, and other issues that might need to be addressed before we can start analyzing the data.
Data exploration helps us understand the structure, distributions, and variable relationships in our data. We can use visualizations like histograms, scatter plots, and box plots to see how the data is distributed and identify any outliers or patterns. This helps us make more informed decisions when analyzing the data.
Performing EDA before statistical modeling helps provide context for analysis by giving us a better understanding of the data we’re working with. It helps us identify any potential issues or biases in the data and make more informed decisions about how to approach the analysis. It’s like putting together a puzzle before you start building a model so you have a clear understanding of the pieces you’re working with.
In conclusion, EDA is a powerful tool for exploring data and gaining insights into the patterns and relationships that exist within the data. By performing EDA before statistical modeling and data analysis, we can gain a better understanding of our data, identify potential issues, and make more informed decisions.