EXPLORATORY DATA ANALYSIS (EDA)
Tags: beginners, datascience, programming, python
Published: 2023-03-02 14:54:22

Exploratory Data Analysis (EDA) is a critical step in data analysis process that involves examining and understanding the characteristics of a [dataset](https://support.google.com/analytics/answer/6014980?hl=en#zippy=%2Cin-this-article).

In this article, we will dive into:

- What is Exploratory Data Analysis (EDA) ?

- Why is it important?

- Common techniques used in EDA

## **What is Exploratory Data Analysis?**

The method of exploring and comprehending data through visualization and summarization of its significant features is what we call Exploratory Data Analysis(EDA).

EDA's objective is to acquire a better understanding of the data and to recognize <u>patterns</u>, <u>trends</u> and <u>correlations</u> that might not be evident initially.

## Steps involved in Exploratory Data Analysis
EDA typically involve several steps which include :

1. **Data Collection** - This involves gathering and compiling 
                         data to be analyzed

2. **Data Cleaning** -  Removal or correction of any errors, 
                        inconsistencies or missing data in the 
                         dataset is done here.

3. **Data Visualization**- This involves creating visualizations 
                          such as histogram , scatter plots and 
                          box plots to explore the data.

4. **Descriptive Statistics** -Here , we calculate the summary 
                              statistics such as mean , median, 
                              standard deviation and correlation 
                               coefficients to summarize the data

5  **Hypothesis Testing** - Hypothesis in other words is an 
                          <u>assumption</u>. This step involves 
                          testing hypotheses/assumptions about the 
                          data in order to determine if the data 
                          is statistically significant.
                          
                          
##  Importance of EDA
EDA is a crucial step in data analysis process because it help us understand the data and identify any potential issue(s) or biases that may affect our analysis and so EDA can help us :

- Identify missing data/outliers that may affect our 
  analysis

- Identify trends and patterns in our data that may not be 
  immediately apparent

- Identify potential relationships or correlations between 
  variables.
 _**Correlation refers to the extent to which two or 
  more variables are related to each other**. If a change in one 
 variable is associated with a change in another variable, the two 
 variables are said to be correlated._

- Identify potential issues or biases that may affect the analysis

- Formulate hypotheses/assumptions about the data that can be 
  tested using statistical methods

Gaining an understanding of the data's characteristics is crucial for making well-informed decisions on how to analyze and interpret the data. Doing so enables us to avoid errors or incorrect conclusions that may be drawn from incomplete or biased analysis.

## Techniques used in Exploratory Data Analysis(EDA)

These techniques help us explore and understand the data. They include :

1.Histograms -This is a graph that shows **frequency 
  distribution** of a dataset. It help in visualizing the shape of 
  the data and identifying unusual patterns
 
2.Box Plots - This are graphical representations of the 
  distribution of a dataset. It displays the <u>minimum</u>, 
  <u>maximum</u>, <u>median</u>, and <u>quartiles</u> of the data, 
  and can help identify unusual patterns in the data. 

3.Scatter Plots - A scatter plot is a graph that shows the 
  **relationship between two variables**. It is useful for 
  identifying any potential correlations or patterns in the data

4.Heat Maps- It involves graphical representation of the data 
  that **uses color** to represent the values in a dataset. It is 
  useful for identifying patterns or correlations in <u>large 
  datasets.</u>

5.Correlation Analysis -This is a statistical technique used to 
  measure the **strength** and **direction** of the relationship 
  between two variables. It is useful for identifying potential 
  relationships or patterns in the data.

## Conclusion
 Exploratory Data Analysis is a critical step in the data analysis 
 process that involves examining and understanding the 
 characteristics of a dataset. By visualizing and summarizing the 
 data, we can identify patterns, trends, and relationships that 
 may not be immediately apparent. This can help us make more 
 informed decisions about how to analyze and interpret the data, 
 and can help us avoid mistakes or incorrect conclusions that may 
 be drawn from an incomplete or biased analysis.