Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is a systematic approach to analyzing data sets in order to summarize their main characteristics, discover patterns, detect anomalies, test assumptions, and check data quality before applying formal statistical models or machine-learning algorithms.

EDA was popularised by John W. Tukey, who emphasized exploration before confirmation.

1. What is Exploratory Data Analysis? EDA is the first and most critical step in data analysis. It focuses on understanding what the data is telling us, rather than immediately applying complex techniques.

Key Ideas:
No prior assumptions about data

Flexible and investigative

Uses both numerical and graphical methods

Helps guide further analysis and modelling

📌 In simple terms:
EDA = “Get to know your data before using it.”

1. Objectives of EDA EDA aims to:

Understand data structure

Summarise key characteristics

Detect outliers and anomalies

Identify patterns and trends

Check assumptions (normality, linearity, etc.)

Assess data quality

Guide feature selection and transformation

Support decision-making

1. Types of Exploratory Data Analysis EDA can be classified based on number of variables and method used:

A. Based on Number of Variables
Type Description
Uni-variate EDA Analysis of one variable
Bi-variate EDA Relationship between two variables
Multivariate EDA Analysis of more than two variables
B. Based on Method
Type Description
Graphical EDA Uses plots and charts
Non-Graphical EDA Uses numerical/statistical measures

1. Steps in Exploratory Data Analysis Step 1: Understand the Data Variable types (categorical, numerical)

Units and scale

Data source

Size of dataset

Step 2: Data Cleaning
Handle missing values

Remove duplicates

Correct inconsistent data

Detect invalid entries

📌 EDA often reveals that real-world data is messy

Step 3: Uni-variate Analysis
Analyzing individual variables.

Numerical Methods:
Mean, Median, Mode

Variance, Standard Deviation

Range, IQR

Skewness, Kurtosis

Percentiles, Z-scores

Graphical Methods:
Histograms

Box plots

Bar charts

Step 4: Bivariate Analysis
Analyzing relationships between two variables.

Numerical Methods:
Correlation

Covariance

Cross-tabulation

Graphical Methods:
Scatter plots

Line plots

Grouped bar charts

Step 5: Multivariate Analysis
Exploring interactions among multiple variables.

Methods:
Correlation matrices

Pair plots

PCA (Principal Component Analysis)

Heatmaps

1. Key Components of EDA A. Measures of Central Tendency Describe the typical value.

Mean

Median

Mode

B. Measures of Dispersion
Describe variability.

Range

Variance

Standard deviation

IQR

C. Measures of Position
Describe relative standing.

Percentiles

Quartiles

Deciles

Z-scores

D. Distribution Shape
Describe how data is distributed.

Skewness (symmetry)

Kurtosis (peakedness)

1. Outlier Detection in EDA Common Methods: IQR method

Z-score method

Visual inspection (box plot)

📌 Outliers may indicate:

Data entry errors

Rare events

Important insights

1. Graphical Tools Used in EDA Tool Purpose Histogram Distribution Box plot Spread & outliers Scatter plot Relationships Bar chart Categorical data Line plot Trends over time Heatmap Correlation strength
2. Importance of EDA EDA: ✔ Prevents incorrect modelling ✔ Improves data quality ✔ Reveals hidden insights ✔ Guides feature engineering ✔ Saves time and resources

📌 Without EDA, conclusions may be misleading.

1. EDA in Data Science & Machine Learning EDA helps in:

Feature selection

Data transformation

Handling skewness

Detecting multicollinearity

Understanding target variable behaviour

1. Advantages of EDA Flexible and intuitive

Minimal assumptions

Works with small and large datasets

Helps explain data to stakeholders

1. Limitations of EDA Subjective interpretation

Cannot prove causation

Time-consuming for large datasets

Results depend on analyst experience

1. Real-World Example Dataset: Customer purchase data

EDA might reveal:

Most customers buy on weekends

Sales are right-skewed

A few customers contribute most revenue

Strong correlation between discounts and sales volume

1. EDA vs Confirmatory Data Analysis EDA Confirmatory Analysis Exploration Hypothesis testing Flexible Structured Pattern discovery Model validation No assumptions Strong assumptions
2. Summary Exploratory Data Analysis is the foundation of all data analysis. It helps analysts understand, clean, summarize, and interpret data, enabling better modelling and accurate decision-making.

“EDA lets the data speak before we impose our theories.”

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