This project involves analyzing the data of Pakistani universities, focusing on various aspects such as the sector (public/private), establishment year, and distribution across provinces. The workflow of this project includes the following steps:
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Data Extraction:
- Downloaded the Pakistani Universities dataset from Kaggle.
- Imported the dataset into a Python environment.
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Data Transformation:
- Cleaned the dataset by dropping null values.
- Performed date formatting.
- Removed unnecessary columns.
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Data Loading:
- Loaded the cleaned data into a MySQL database, which serves as Data Warehouse.
- Created a single table in the DW to store the data.
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Dashboarding in Tableau:
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Created visualizations to explore and present the data:
- Sector of Universities: Displayed a bar chart of public and private universities.
- Universities Established Each Year: Showcased a bar chart illustrating the number of universities established each year.
- Universities Table: Provided a comprehensive table of all universities.
- Universities in Each Province: Visualized the distribution of universities across different provinces in Pakistan.
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ETL Process:
- Screenshot of VS Code terminal with "ETL Performed Successfully" message.
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Data Loading Confirmation:
- Screenshot of MySQL terminal showing
SELECT * FROM Uni_list LIMIT 10query result, confirming data load. 
- Screenshot of MySQL terminal showing
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Tableau Dashboard:
- Screenshot of Tableau Dashboard.
To reproduce this project, follow these steps:
- Clone the repository.
- Install the required Python libraries.
- Execute the ETL script.
- Import the Tableau dashboard to explore the visualizations.
- Python for data extraction and transformation.
- MySQL (as the Data Warehouse) for data storage.
- Tableau for data visualization.
This project is licensed under the MIT License - see the LICENSE file for details.