What is Exploratory Data Analysis?

EDA is a tool that is used by Data Scientists which often involves the use of data visualization techniques to analyze, understand and summarize data set's key features.

EDA makes it simpler for data scientists to find patterns, identify anomalies, test hypotheses and identify assumptions to provide answers

EDA offers knowledge of data set variables and the interactions between them. It is mostly used to look into what data can provide beyond the formal modelling. It can also assist in determining the accuracy of the statistical methods you are considering for data analysis.

Importance of EDA in data science

EDA's primary goal is to help in examining data before making any conclusions. It can help in correcting obvious mistakes, better understanding data patterns, spotting patterns or unusual patterns and discovering links between the variables.

Exploratory analysis is a tool that data scientists use to make sure the results they provide are accurate and applicable to any business or company goals. By ensuring stakeholders are posing important questions, EDA also benefits them. Standard variation, Quantitative variables and confidence intervals are among the topics that EDA may assist with. Elements of EDA may be applied to more complex data analysis or modelling such as machine learning.

Tools

• python : In order to determine how to handle missing values for machine learning, it is crucial to be able to discover missing values in a data set using Python and EDA combined.

• R : When making statistical observations and performing data analysis, statisticians in the field of data science frequently utilize the R language.

Types of Exploratory Data Analysis

• univariate non geographical
  This is simplest form of data analysis, where the data being analyzed consists of just one variable. Since it’s a single variable, it doesn’t deal with causes or relationships. The main purpose of univariate analysis is to describe the data and find patterns that exist within it.

• univariate geographical
  Non-graphical methods don’t provide a full picture of the data. Graphical methods are therefore required.

• Multivariate nongraphical
  Multivariate data arises from more than one variable. Multivariate non-graphical EDA techniques generally show the relationship between two or more variables of the data through cross-tabulation or statistics.

• Multivariate graphical
  Multivariate data uses graphics to display relationships between two or more sets of data. The most used graphic is a grouped bar plot or bar chart with each group representing one level of one of the variables and each bar within a group representing the levels of the other variable.