What is explanatory Data Analysis

Exploratory Data Analysis (EDA) is an analysis approach that identifies general patterns in the data.

EDA involves the following activities

Data visualization-using plots and graphs to visually inspect the data

Correlation analysis-a statistical measure that expresses the extent to which two variables are linearly related

Outlier detection- is the process of detecting outliers, or a data point that is far away from the average, and depending on what you are trying to accomplish, potentially removing or resolving them from the analysis to prevent any potential skewing.

Descriptive Statistics-a branch of statistics that involves summarizing, organizing, and presenting data meaningfully and concisely

Importance of EDA
EDA is important because it helps analysts to understand data before applying any advanced analytical methods

• detects mistakes by inspecting the data visually it helps spot errors that could skew the results of the analysis

• Understands the distribution of variables which helps in choosing the right statistical tests and models.

• it helps reveal the quality of the data allowing the analyst make informed decisions about cleaning and processing

• Helps generate hypothesis by exploring data which gives insights for further testing

Key techniques in EDA

1. Univariate Analysis:it consists of data that consists of observations on only one characteristic or attribute. There is only one variable in univariate data. The analysis of univariate data is thus the most basic type of analysis because it deals with only one variable that changes.
   You can use graphical representations such as histograms, box plots, and pie charts to better understand the distribution, central tendency, and spread of the data

2. Bivariate Analysis:it is a statistical method examining how two different things are related.

3. Multivariate Analysis:Also known as MVA it involves evaluating multiple variables (more than two) to identify any possible association among them.The techniques are especially valuable when working with correlated variables.

4*Data Cleaning and Preprocessing* it eliminates outliers, which impact data analysis. Outliers are values that are considerably different from the other values in the dataset.Handling missing data, removing duplicates, and correcting data types.

Conclusion

Exploratory Data Analysis is the foundation of all data science projects. EDA provides a clear knowledge of the data, paving the way for more accurate and insightful analysis. EDA uncovers hidden patterns and relationships in data by using descriptive statistics, visualizations, and numerous analytical tools. As a result, it allows analysts and data scientists to make more informed decisions, choose relevant models, and effectively convey their findings.

Mastering EDA is critical for anybody hoping to thrive in data science since it establishes the framework for all subsequent studies and ensures that your models are robust and dependable.