Education Sector EDA

Project Overview

This project focuses on Exploratory Data Analysis (EDA) of the Engineering Education sector in Maharashtra.
It is a mixed academic, internship, and portfolio project conducted during my internship at Clustor Computing.

The goal of this project is to analyze, clean, visualize, and interpret education-related data to understand trends in engineering colleges, universities, student intake, and related parameters using Python and Power BI.

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Project Objectives

• Collect education sector data from multiple sources
• Clean and preprocess raw datasets
• Perform Exploratory Data Analysis (EDA)
• Extract meaningful features and patterns
• Create visualizations and dashboards for insights
• Present findings in a structured and understandable format

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Data Sources

• Government education portals (e.g. data.gov.in)
• University and college websites
• AICTE / MSBTE related sources
• Publicly available education datasets

(Data used is non-confidential and for academic analysis purposes.)

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Tools & Technologies Used

• Python
• Pandas, NumPy
• Matplotlib, Seaborn
• Jupyter Notebook
• Power BI
• Excel
• Git & GitHub

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Project Workflow

1. Project planning and scope definition
2. Data collection from multiple sources
3. Data cleaning (missing values, duplicates, standardization)
4. Exploratory Data Analysis (EDA)
5. Feature extraction and correlation analysis
6. Data visualization using Python
7. Dashboard creation using Power BI
8. Documentation and presentation

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Folder Structure

education-sector-eda/
├── README.md
│
├── code/
│   ├── eda_analysis.ipynb
│   └── data_cleaning.py
│
├── outputs/
│   └── data_cleaning_visualization.html
│
├── dashboards/
│   └── power_bi_dashboard.pdf
│
├── reports/
│   ├── eda_feature_extraction.pdf
│   └── sprint_execution_report.pdf
│
└── presentation/
    └── education_eda_presentation.pptx

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Key Insights

• Engineering colleges and student intake vary significantly across cities
• University-wise pass-out trends show noticeable differences
• Data quality issues required careful preprocessing
• Visualization helped identify patterns not obvious in raw data

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Role & Contribution

• Data collection and validation
• Data cleaning and preprocessing
• Exploratory Data Analysis using Python
• Feature extraction
• Visualization and dashboard creation
• Documentation and presentation

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Project Type

• Internship Project (Clustor Computing)
• Academic Project
• Data Analytics Portfolio Project

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Author

Vaibhav P Adikane

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Note

This project is created for learning, academic, and portfolio purposes and demonstrates practical application of data analytics and visualization techniques in the education domain.