Decoding Higher Education

A Trend Analysis of Industrial Engineering Programs in Turkey

Understanding the hidden drivers behind student demand, university prestige, and regional educational dynamics through data analytics.

Explore Analysis View Dataset



Team Let It Happen

2018–2024

Years Analyzed

100+

IE Programs

81

Cities Covered

Multi

Integrated Sources

Project Overview

Industrial Engineering programs in Turkey exhibit significant differences in popularity, student demand, and ranking performance. However, raw placement statistics alone often fail to explain why certain universities consistently outperform others.

This project investigates the evolution of Industrial Engineering departments between 2018 and 2024 by combining official placement statistics, demand indicators, and regional characteristics.

Using R and Quarto, we apply data analytics techniques to explore how geographical location, quota structures, and demand intensity relate to departmental success and national visibility.

Research Questions

  • What factors influence the popularity of Industrial Engineering departments in Turkey?
  • Do metropolitan universities dominate student demand?
  • How does the demand-to-quota ratio affect ranking performance?
  • Which universities demonstrate the strongest upward trends over time?
  • Are regional patterns observable across the Turkish higher education landscape?

Methodology Overview

This project combines modern data analytics workflows with reproducible research practices.

Analytical Pipeline: - Data extraction using the thestats R package - Integration of YÖK Atlas and supplementary Kaggle datasets - Data cleaning and preprocessing in R - Exploratory Data Analysis (EDA) - Machine Learning & Correlation Analysis - Statistical modeling and visualization through Quarto

Key Takeaways

  • Metropolitan Dominance: Universities in major economic hubs consistently dominate ranking performance and student demand.
  • Demand Exclusivity: The demand-to-quota ratio is strictly associated with national success rankings and top-tier placements.
  • Upward Trends: Several Anatolian universities demonstrate rapid upward academic trends after 2021.
  • Sector Strategies: Foundation universities actively manipulate low scholarship quotas to artificially boost their selectivity metrics.
  • Data-Driven Clarity: Combining Machine Learning with EDA provides a profound operational view beyond raw placement statistics.

Research Team

Project Contributors

This project was developed collaboratively as part of EMU430 Data Analytics, focusing on reproducible research, statistical exploration, and higher education trend analysis.

Yusuf Bora Çavdar

Research Design · Visualization · Modeling

Onur Furkan Gök

Lead Data Analysis · Data Processing · Methodology