Exploratory data analysis (EDA) involves using statistics and visualizations to analyze and identify trends in data sets. The primary intent of EDA is to determine whether a predictive model is a feasible analytical tool for business challenges or not. EDA helps data scientists gain an understanding of the data set beyond the formal modeling or hypothesis testing task. Exploratory data analysis is essential for any research analysis, so as to gain insights into a data set. In this article, let’s take a look at the importance, and purpose, and objective of exploratory data analysis that an analyst would want to extract from a data set.
Learn everything there is to know about exploratory data analysis in this blog, a method for evaluating and drawing conclusions from large amounts of data.
Why Is EDA Important?
Exploratory data analysis is essential for any business. It allows data scientists to analyze the data before coming to any assumption. It ensures that the results produced are valid and applicable to business outcomes and goals.
Importance of using EDA for analyzing data sets is:
- Helps identify errors in data sets.
- Gives a better understanding of the data set.
- Helps detect outliers or anomalous events.
- Helps understand data set variables and the relationship among them.
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Why Do We Perform Exploratory Data Analysis?
The primary objective of exploratory data analysis in order to perform exploratory data analysis is to uncover the underlying structure. The structure of the various data sets determines the trends, patterns, and relationships among them. A business cannot come to a final conclusion or draw assumptions from a huge quantity of data and rather requires taking an exhaustive look at the data set through an analytical lens.
Therefore, performing an Exploratory Data Analysis allows data scientists to detect errors, debunk assumptions, and much more to ultimately select an appropriate predictive model.
Objective of Exploratory Data Analysis
The goal of EDA is to allow data scientists to get deep insight into a data set and at the same time provide specific outcomes that a data scientist would want to extract from the data set. It includes:
- List of outliers
- Estimates for parameters
- Uncertainties for those estimates
- List of all important factors
- Conclusions or assumptions as to whether certain individual factors are statistically essential
- Optimal settings
- A good predictive model
Exploratory data analysis comes in handy whenever a data scientist needs to gain new insights into a massive quantity of data sets. In this aspect, EDA can be beneficial for fields such as research and development, engineering, and data science. Hence, In today’s age, with access to advanced computing power along with the support of modern analytics. EDA can be a stimulating and engaging experience for researchers or data scientists to explore unexpected value in a massive quantity of complex data sets.