What is EDA in simple words?
Exploratory Data Analysis (EDA) is an analysis approach that identifies general patterns in the data. These patterns include outliers and features of the data that might be unexpected. EDA is an important first step in any data analysis.There are dress shoes, hiking boots, sandals, etc. Using EDA, you are open to the fact that any number of people might buy any number of different types of shoes. You visualize the data using exploratory data analysis to find that most customers buy 1-3 different types of shoes.In conclusion, there are several different types of exploratory data analysis, including univariate, bivariate, and multivariate EDA. Within each of these types, there are both graphical and non-graphical methods for exploring the data.

What is the purpose of exploratory data analysis : The main purpose of EDA is to help look at data before making any assumptions. It can help identify obvious errors, as well as better understand patterns within the data, detect outliers or anomalous events, find interesting relations among the variables.

What is EDA and its types

Exploratory Data Analysis is a process of examining or understanding the data and extracting insights dataset to identify patterns or main characteristics of the data. EDA is generally classified into two methods, i.e. graphical analysis and non-graphical analysis.

What are the steps in EDA : Let's look at how to perform EDA using python!

  1. Step 1: Import Python Libraries.
  2. Step 2: Reading Dataset.
  3. Step 3: Data Reduction.
  4. Step 4: Feature Engineering.
  5. Step 5: Creating Features.
  6. Step 6: Data Cleaning/Wrangling.
  7. Step 7: EDA Exploratory Data Analysis.
  8. Step 8: Statistics Summary.

Exploratory Data Analysis (EDA) is a process of describing the data by means of statistical and visualization techniques in order to bring important aspects of that data into focus for further analysis.

Exploratory Data Analysis Techniques

  • Univariate Non-Graphical. This is the simplest type of EDA, where data has a single variable.
  • Univariate Graphical. Non-graphical techniques do not present the complete picture of data.
  • Multivariate Non-Graphical. Multivariate data consists of several variables.
  • Multivariate Graphical.

What is the basic EDA analysis

EDA techniques may include calculating summary statistics, visualizing data distributions, identifying outliers, exploring relationships between variables, and performing hypothesis testing. This process helps gain insights into the data, identify patterns, and inform further analysis or decision-making.In conclusion, EDA plays a pivotal role in data analysis and business growth. It helps stakeholders understand their needs, uncover hidden patterns, identify data quality issues, enhance decision-making, mitigate risks, enable agile decision-making, and improve communication and collaboration.EDA is the process of investigating the dataset to discover patterns, and anomalies (outliers), and form hypotheses based on our understanding of the dataset. EDA involves generating summary statistics for numerical data in the dataset and creating various graphical representations to understand the data better.

EDA is the process of investigating the dataset to discover patterns, and anomalies (outliers), and form hypotheses based on our understanding of the dataset. EDA involves generating summary statistics for numerical data in the dataset and creating various graphical representations to understand the data better.

What is EDA and how to do it : Exploratory Data Analysis (EDA) refers to the method of studying and exploring record sets to apprehend their predominant traits, discover patterns, locate outliers, and identify relationships between variables.

Is EDA and ETL same : ETL refers to the process of extracting data from multiple sources, transforming it into a consistent format, and loading it into a target database for further analysis. On the other hand, EDA focuses on examining and understanding raw datasets to gain insights before any transformation or modeling takes place.

What are the main steps in EDA

Steps Involved in Exploratory Data Analysis (EDA)

  • Data Collection.
  • Finding all Variables and Understanding Them.
  • Cleaning the Dataset.
  • Identify Correlated Variables.
  • Choosing the Right Statistical Methods.
  • Visualizing and Analyzing Results.


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) refers to the method of studying and exploring record sets to apprehend their predominant traits, discover patterns, locate outliers, and identify relationships between variables.

How to do basic EDA : Steps Involved in Exploratory Data Analysis

  1. Data Collection. Data collection is an essential part of exploratory data analysis.
  2. 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.
  3. Univariate Analysis.
  4. Bivariate Analysis.