Exploratory Data Analysis Ultimate Guide
Tags: programming, datascience, python, codenewbie
Published: 2023-02-27 13:55:42

![analysis](https://imgs.search.brave.com/qlmozpLzJJEM4kzJt8WLbL4xLLU39FInw2_BzhkCl4c/rs:fit:1140:225:1/g:ce/aHR0cHM6Ly90c2Uy/Lm1tLmJpbmcubmV0/L3RoP2lkPU9JUC5L/RDh6aldQZ2dQWWhn/RjBZTzBseVRnSGFE/RiZwaWQ9QXBp)

Exploratory Data Analysis (EDA) involves examining and understanding the structure and characteristics of a dataset to uncover patterns, relationships, and anomalies. EDA involves the use of statistical and visualization techniques to gain insights into the data before applying any machine learning algorithms. In this ultimate guide, we will explore what EDA is, why it is important, and the various techniques and tools used in EDA.

## What is EDA?

Exploratory Data Analysis is the process of visualizing and analyzing data to uncover hidden patterns, relationships, and trends. EDA involves examining the data to understand the distribution, outliers, and missing values. The primary goal of EDA is to gain insights into the data and identify potential issues that may affect the results of any analysis.

## Why is EDA Important?

EDA is essential for several reasons. Firstly, it helps to identify any anomalies or outliers in the data that can skew the analysis results. Secondly, it helps to understand the distribution of the data, which is crucial in selecting the appropriate statistical tests and models. Thirdly, EDA helps to identify any patterns or relationships between variables, which can be used to create better predictive models.

## EDA Techniques and Tools

EDA involves the use of several techniques and tools to gain insights into the data. Here are some of the common techniques and tools used in EDA.

**Descriptive Statistics**: Descriptive statistics involves summarizing the data using measures such as mean, median, mode, standard deviation, and range. Descriptive statistics help to understand the central tendency and spread of the data.

**Visualization**: Visualization techniques such as histograms, box plots, scatter plots, and heat maps are used to visualize the distribution, outliers, and relationships in the data.

**Correlation Analysis**: Correlation analysis is used to measure the strength and direction of the relationship between two variables. Correlation analysis can be used to identify any patterns or relationships in the data.

**Dimensionality Reduction**: Dimensionality reduction techniques such as Principal Component Analysis (PCA) and t-SNE are used to reduce the number of variables in the data while preserving the underlying structure.

**Clustering**: Clustering techniques such as K-Means and Hierarchical clustering are used to group similar data points together based on their similarity.

**Text Analysis**: Text analysis techniques such as sentiment analysis and topic modeling are used to gain insights into unstructured data such as text.

## Tools used in EDA

**Python**: Python is a popular programming language for data science and has several libraries such as pandas, matplotlib, and seaborn, which are widely used in EDA.

**R**: R is a programming language and environment for statistical computing and graphics. R has several libraries such as ggplot2, dplyr, and tidyr, which are widely used in EDA.

**Tableau**: Tableau is a data visualization tool that allows users to create interactive dashboards and visualizations.

**Excel**: Excel is a widely used spreadsheet program that has several built-in features for data analysis such as pivot tables, charts, and formulas.

## Conclusion

Exploratory Data Analysis is a crucial step in data science that helps to gain insights into the data before applying any machine learning algorithms. EDA involves the use of several techniques and tools to understand the distribution, outliers, and relationships in the data. By performing EDA, data scientists can identify any issues with the data and create better predictive models.

I'll publish another article looking into a deep dive into Exploratory Data Analysis.

##Resources

Here are some amazing resources I came across;

- [kaggle](https://www.kaggle.com/code/laurenlavelle/eda-it-salary-survey-2020)
- [datacamp](campus.datacamp.com)