Singapore Universities Analysis

Project Overview

The dataset contains data regarding graduate employment outcomes, salaries, and related metrics. The goal was to critique the dataset, model its structure, implement it as a MySQL database, and create a simple Node.js-based web application to query and display insights from the data.

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Key Project Stages

1. Dataset Analysis and Critique:
   • Dataset: GES dataset of Singapore universities.
   • Critique:
     • Examined the dataset for completeness, consistency, and relevance.
     • Identified data fields such as university names, courses, employment rates, median salaries, and job types.

• Questions Formulated:
  • What is the average Full-Time Permanent (FTP) and Overall Employment Rates for each university?
  • What is the trend in the highest mean and median salaries for each year from 2013 to 2021, along with the corresponding university and degree?
  • Which degree is facing a declining Full-Time Permanent (FTP) and Overall Employment Rates?

1. E/R Model Creation:
   • Entity-Relationship Model:
     • Modeled entities such as Universities, Courses, Graduates, and Employment Outcomes.
   
   • Defined relationships, such as:
     • “Universities offer Courses.”
     • “Graduates complete Courses.”
     • “Graduates have Employment Outcomes.”
   • Adjusted for relational database implementation:
     • Normalized the design to reduce redundancy.
     • Incorporated primary and foreign keys for all relationships.
2. MySQL Database Implementation:
   • Built a relational database in MySQL based on the E/R model.
   
   • Populated the database with sufficient data to ensure:
     • All tables had meaningful entries.
     • Relationships were demonstrated through multiple queries.
   • Data Populated:
     • Universities: Names, types (public/private).
     • Courses: Names, fields, and associated universities.
     • Employment Outcomes: Salaries, job types, and rates.
3. Web Application Development:
   • Built a simple Node.js-based web application to interact with the MySQL database.
   • Implemented functionalities to:
     • Display lists of universities and courses.
     • Query employment outcomes and salaries.
     • Answer formulated questions dynamically (e.g., “Which courses offer the highest mean salaries?”).

• Technologies Used:
  • Node.js for server-side scripting.
  • MySQL as the backend database.
  • Basic HTML/CSS/JavaScript for the user interface.

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Deliverables

1. Report:
   • Critique of the dataset.
   • E/R diagrams illustrating the database structure.
   • Queries demonstrating answers to key questions.
2. MySQL Database:
   • Populated with GES data.
   • Normalized and optimized for queries.
3. Node.js Web Application:
   • Accessible via a shareable link to a lab environment.
   • Interface designed to display and query GES data.
4. Dataset Source URL:
   • Provided a link to the original dataset for verification and replication.

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Project Outcomes

This project successfully demonstrated the ability to analyze a dataset, model it using E/R diagrams, implement a relational database, and develop a web application to query and visualize data. It highlights a strong understanding of database design, web development, and the practical application of data analysis to answer real-world questions.