It involves visually and statistically examining a dataset to understand its main characteristics, uncover patterns, spot anomalies, and generate hypotheses
It's the process of visual and statistical examination of data (exploring & observing), and understanding the dataset (it's characteristics, patterns, and anomalies), to generate a generate hypotheses or plan the data science experiment:
Methods

• Descriptive Statistics
• Summary statistics.
• Use data exploration tools.

Benefits:

• Quality Control
• Hypothesis Generation
• Understanding Data
• Data Visualization

Applications:

• Data Preparation
  • Find any Missing Values, wrong data-types values, duplicates.
  • Drop useless columns
  • Rename columns
  • Drop irrelevant data
  • Categorize your values
  • Discover and drop skewed or imbalanced data
  • Create new features
• Feature Extraction: Feature selection is about selecting attributes that have the greatest impact towards the problem you are solving.
  • Specify features
  • Discover important features (or attributes)
  • Identify relationships and correlations in features
• Relationship exploration
• Data summarization
• Pattern discovery
• Uni-variate analysis: Descriptive Statistics on one single variable(E.g. Categorical Variables and numeric variables with measures such as Central Tendency, Dispersion, Shape).
• B-variate analysis: Descriptive Statistics between two variables.
• Multi-variate analysis: Descriptive Statistics between more than two variables. ℹ️ Scatter Plot, Correlation Plot, and Heat Map are often used in Bi-variate and Multi-variate analysis.
• Locate any outliers in your dataset