NUML Faculty Analysis

Project Overview

This project involves analyzing the faculty data of NUML's Department of Computer Science (CS) and Department of Software Engineering (SE). The analysis focuses on comparing faculty members across various aspects such as:

• The number of faculty members in each department.
• Designation-wise comparison (e.g., Associate Professors, Assistant Professors, and Lecturers).
• PhD qualification comparison.
• Comprehensive list of all faculty members with their designations, department, and contact information.

The workflow of this project includes the following steps:

1. Data Collection:

   • Scraped faculty data from the NUML website for both CS and SE departments.
   • Organized data into a structured format.

2. Data Transformation:

   • Cleaned and formatted the data (e.g., handling missing fields, standardizing designations).
   • Categorized faculty members by their designations and departments.

3. Data Analysis:

   • Performed comparisons between the CS and SE departments on:
     • Total number of faculty members.
     • Designation distribution (Lecturers, Assistant Professors, Associate Professors).
     • Number of PhD-qualified faculty members.

4. Visualization:

   • Created visualizations to illustrate:
     • Faculty distribution in CS and SE departments.
     • Designation-wise comparison.
     • PhD qualification comparison.
     • List of all faculty members with their designations, departments, and contact details, color-coded as follows:
       • Red: Lecturer
       • Yellow: Associate Professor
       • Orange: Assistant Professor

Project Workflow

Screenshots

1. ETL Process:

   • Screenshot of VS Code terminal with "ETL Performed Successfully" message.
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2. Analysis Visualizations:

   • Screenshot of Tableau Dashboard.
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Getting Started

To reproduce this project, follow these steps:

1. Clone the repository.
2. Install the required Python libraries.
3. Execute the data scraping and transformation script.
4. View the analysis and visualizations.

Technologies Used

• Python for data scraping, transformation, and analysis.
• Tableau for creating visualizations.

License

This project is licensed under the MIT License - see the LICENSE file for details.