🧠 Exploratory-Data-Analysis-Tutorial

A complete learning repository covering Exploratory Data Analysis (EDA) from theory to practice — created specially for students to master data understanding, cleaning, and visualization techniques in Python.

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📘 Overview

This repository serves as a comprehensive guide to learning EDA both conceptually and practically.
It contains two main components:

• 🧾 Theory File: Explains every EDA concept including data types, summary statistics, missing values, outliers, correlation, distributions, and visualization techniques.
• 💻 Practical Notebook: A complete hands-on EDA project using the Titanic dataset (from Seaborn), demonstrating every concept step-by-step in Python.

This repository helps students connect theory with real implementation, making EDA easy and engaging to learn.

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🔍 What You’ll Learn

✅ Understanding different types of data
✅ Handling missing and duplicate values
✅ Detecting and treating outliers
✅ Exploring numerical and categorical features
✅ Correlation analysis and feature relationships
✅ Data visualization using Matplotlib and Seaborn
✅ Drawing meaningful insights and EDA summaries

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🧩 Dataset Used

Dataset: Titanic (available in Seaborn library)

import seaborn as sns  
titanic = sns.load_dataset('titanic')

The Titanic dataset is ideal for practicing EDA — it involves passenger survival data and helps learners explore relationships between features like age, gender, class, and survival status.

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🛠️ Tools & Libraries

• Python 3.x
• Pandas – Data handling and cleaning
• NumPy – Numerical operations
• Matplotlib – Visualization
• Seaborn – Statistical graphics
• Jupyter Notebook – Interactive code execution

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📂 Repository Structure

EDA-Theory-and-Practice/
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├── 📘 EDA_Method_Theory.ipynb        # Complete EDA theory notes
├── 💻 EDA_Method_Practise.ipynb     # Practical EDA notebook    
└── LICENSE                  # MIT License file

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⚙️ How to Use

1. Clone this repository:

   git clone https://github.com/yourusername/Exploratory-Data-Analysis-Tutorial.git

2. Open the Jupyter Notebook (Titanic_EDA.ipynb).

3. Run each cell and follow the step-by-step EDA workflow.

4. Refer to EDA_Method_Theory.pdf for theoretical explanations.

5. Use outputs and plots to interpret and summarize your findings.

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🎯 Learning Outcomes

By the end of this module, you will:

• Understand EDA principles thoroughly
• Be able to clean and analyze raw data efficiently
• Visualize relationships and patterns effectively
• Gain confidence in preparing datasets for machine learning

This project prepares you for real-world data science tasks and interview-level EDA questions.

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🧑‍🏫 Author

Vishnu V Unnikrishnan 📍 Data Science & AI Faculty | IPCS Global, Bangalore 💬 Dedicated to teaching Data Science, Machine Learning, and AI through hands-on, project-based learning.

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🪪 License

This project is licensed under the MIT License — feel free to use, share, and modify for learning or educational purposes, with proper attribution.