Exploratory data analysis(EDA) is an approach to analyze the data to summarize their main characteristics, often using statistical graphics and other data visualization methods. EDA is all about making sense of data in hand, before getting them dirty with it. EDA is similar to the art of storytelling, a story that data is trying to tell.
“Data science is the field of study that combines domain expertise, programming skills, and knowledge of mathematics and statistics to extract meaningful insights from data.”
By conducting EDA, we can achieve the major role of Data Science as like providing meaningful insight into the data as to how the variables are correlated to each other and how it is impacting our Business. EDA gives a better understanding of the Variables and the Relationship between them along with the Pattern Identification of the Distribution. From which we can understand the spread of the data and how it is been distributed. Even with the high level of domain knowledge, after performing the EDA, we will be able to understand the Business much better on the basis of how each variable is correlated to each other.
The Main Components of exploring data
- Understanding the variables in the dataset
- Cleaning and Identifying the Patterns in the dataset
- Analyzing relationships between variables in our dataset
By going through the exploratory data analysis, we’ll have a much better understanding of our data along with the business process, which will make it easier to choose our model, our attributes, and refine it overall. This piece of work(EDA), will make a huge change in our Data Modelling which will also return to a more accurate and appropriate solution for our Business.