It’s the process of exploring, summarizing, and visualizing a dataset to understand its key characteristics before applying more advanced modeling or statistical techniques.
To understand the structure of the data
To detect patterns, trends, and relationships
To identify anomalies or outliers
To check assumptions for statistical models
To guide further analysis or feature engineering
Data Collection & Loading – Import data from sources like CSV, databases, or APIs.
Data Inspection – View dimensions, data types, and sample rows (head() in pandas).
Data Cleaning – Handle missing values, duplicates, and incorrect data types.
Descriptive Statistics – Compute measures like mean, median, standard deviation.
Data Visualization – Use plots like histograms, scatter plots, box plots, and heatmaps.
Correlation & Relationship Analysis – Check how variables relate to each other.
Outlier Detection – Identify data points that deviate significantly from the rest.
Python: Pandas, NumPy, Matplotlib, Seaborn, Plotly
R: ggplot2, dplyr, tidyverse
To identify anomalies or outliers
To check assumptions for statistical models
To guide further analysis or feature engineering