US_Top_50_Universities_2026 Analysis

Author

Pascal Hermann Kouogang Tafo

INTRODUCTION

This project consists of analyzing the 2026 US Top 50 Universities dataset to evaluate the relationship between institutional characteristics, specifically focusing on research impact and employment outcomes.By analyzing research impact and post-graduation employment rates, we aim to identify performance trends across public and private institutions for the 2026 academic outlook.

APPROACH

To conduct the analysis of the dataset, i will implement a structured data science pipeline using the “tidyverse” framework as followed:

  1. Load the CSV dataset in R and commit to a GitHub repository ensuring its accessibility at anytime .

  2. Rename some variables by removing to standardize naming convention.

  3. Transform the dataset into a long format, which allows for more efficient faceted plotting and statistical comparison

  4. Compute descriptive statistics grouped by institution type to establish baselines for Research_Impact_Score and Employment_Rate.

  5. Construct plots to compare research impact distributions between public and private institutions, identifying variability and outliers.

  6. Compute a correlation analysis to determine if higher research impact scores are significantly associated with higher employment rates for each institution type.

CONCLUSION