Day 4 | Exploratory Data Analysis (EDA) in Python | Distributions, Correlation & Visualization

Описание: Welcome to Day 4 of the Data Science & Analytics Internship.

This session focuses on Exploratory Data Analysis (EDA)—a critical step performed after data cleaning to understand how data behaves before building any predictive models.

Using Python, students learn how to analyze both numerical and categorical variables through summary statistics and visual exploration. The class demonstrates how to interpret histograms, box plots, scatter plots, grouped comparisons, and correlation heatmaps to uncover meaningful patterns in student performance data.

Special attention is given to core statistical ideas such as:

Distribution, spread, and outliers

Normal distribution and skewness

Mean vs. median interpretation

Bivariate relationships between variables

Correlation analysis and heatmaps

Understanding why correlation does not imply causation

This session helps interns build a strong analytical foundation, enabling them to move from raw data toward informed decision-making and future predictive modeling.

Learning Outcomes

By the end of this session, interns will:

Apply practical EDA techniques in Python

Interpret data distributions and variability

Explore relationships between variables visually and statistically

Understand the limitations of exploratory analysis and the need for deeper validation

Program: Data Science & Analytics Internship
Session Focus: Exploratory Data Analysis (EDA)
Tools & Skills: Python, Data Visualization, Statistical Analysis
Duration: 5 Hours