Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns, to spot anomalies, to test hypothesis and to check assumptions with the help of summary statistics and graphical representations.

Steps Involved in Exploratory Data Analysis

Data Collection. Data collection is an essential part of exploratory data analysis.

Data Cleaning. Data cleaning refers to the process of removing unwanted variables and values from your dataset and getting rid of any irregularities in it.

Univariate Analysis.

Bivariate Analysis.

Exploratory data analysis (EDA) is used by our data scientists to analyse 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.

The following are various types of visualizations used by us –

Univariate analysis: This type of data consists of only one variable. The analysis of univariate data is thus the simplest form of analysis since the information deals with only one quantity that changes. It does not deal with causes or relationships and the main purpose of the analysis is to describe the data and find patterns that exist within it.

Bi-Variate analysis: This type of data involves two different variables. The analysis of this type of data deals with causes and relationships and the analysis is done to find out the relationship among the two variables.

Multi-Variate analysis: When the data involves three or more variables, it is categorized under multivariate.