MODULE 4 OF 8 · 20 Hrs · 2 Weeks

Exploratory Data Analysis (EDA)

Before any model is built, a dataset has to be understood. This module builds the systematic exploration discipline that uncovers patterns, relationships and anomalies — the insight-generating step every modelling decision depends on.

Who This Module Is For

Students who have completed the data wrangling module and are ready to explore real datasets systematically.

Real-World Relevance

Structured EDA is the step that separates a data scientist who understands their data from one who jumps straight to modelling — and it's one of the most commonly assessed skills in take-home interview exercises.

Program Overview View Hands-On Labs

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Curriculum

What You Will Learn

A detailed, industry-aligned breakdown of every topic covered in this module.

Univariate, bivariate & multivariate analysis
Summary statistics & data profiling
Identifying patterns, trends & anomalies
Correlation analysis between variables
Outlier detection techniques
Handling skewed distributions
Hypothesis-driven data exploration
Communicating early insights clearly

Technology Stack

Tools You Will Use

Hands-on time with the same tools used by working data analysts and data scientists today.

Pandas

Data manipulation library used to clean, transform and analyse structured datasets.

NumPy

Numerical computing library used for array operations and mathematical computation in data science.

Jupyter Notebook

Interactive notebook environment used for data exploration, analysis and model prototyping.

Pandas Profiling

Automated data-profiling tool used to generate quick exploratory data analysis reports.

Practical Work

Hands-On Labs

Production-style data science lab scenarios, built using real, messy datasets.

Profile a real dataset and summarise its structure, quality and key statistics.

Run univariate, bivariate and multivariate analysis on a real dataset.

Detect and handle outliers and skewed distributions in sample data.

Analyse correlations between variables and surface early patterns.

Document and present exploratory findings in a clear, structured brief.

Evaluation

Assessment

Knowledge Assessment

Quiz covering univariate/bivariate analysis, correlation and outlier-detection techniques.

Practical Evaluation

Students must produce a full exploratory data analysis report on an assigned dataset, including key patterns and anomalies found.

Portfolio

Projects

Industry-style deliverables added directly to your project portfolio.

Portfolio Project 01

EDA & Visualization Project

Explore a dataset end-to-end and present findings through Matplotlib and Seaborn visuals.

Module Outcome

What This Module Builds

Students learn to systematically explore datasets to uncover patterns and generate the insights that guide every modeling decision that follows.

Maps to job roles

Data AnalystInsights AnalystJunior Data Scientist — foundation trackReporting Analyst