Big Data Analytics of Socio-Economic Impact on UK University Graduates

𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄
This project explores the 𝗶𝗺𝗽𝗮𝗰𝘁 of 𝘀𝗼𝗰𝗶𝗼-𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 factors on 𝗨𝗞 university graduates using 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮 analytics. We analyzed large-scale datasets to determine how 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 backgrounds influence student performance, dropout rates, and career prospects after graduation.

𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮
By leveraging 𝗺𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 models, 𝗽𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀, and 𝗱𝗮𝘁𝗮 𝘃𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 techniques, we processed vast datasets from government sources, university reports, and economic indicators. The study involved:

✔ 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 & 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 – Handling raw datasets, removing inconsistencies, and ensuring accuracy.
✔ 𝗗𝗮𝘁𝗮 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 & 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 – Structuring data for advanced analysis using Python libraries.
✔ 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 & 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 – Using 𝗔𝗜 to identify trends and predict future patterns.
✔ 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 – Creating charts, heatmaps, and correlation graphs for clear insights.

𝗞𝗲𝘆 𝗙𝗶𝗻𝗱𝗶𝗻𝗴𝘀
📌 Students from lower 𝘀𝗼𝗰𝗶𝗼-𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 backgrounds struggle more academically and have higher dropout rates.
📌 Graduates from wealthier regions secure better job opportunities and higher salaries.
📌 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 support, university funding, and local economic conditions significantly impact student success.

𝗧𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝗶𝗲𝘀 & 𝗧𝗼𝗼𝗹𝘀 𝗨𝘀𝗲𝗱
🖥 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗼𝗹𝗮𝗯, 𝗽𝗮𝗻𝗱𝗮𝘀, 𝗻𝘂𝗺𝗽𝘆, 𝗺𝗮𝘁𝗽𝗹𝗼𝘁𝗹𝗶𝗯, 𝘀𝗲𝗮𝗿𝗻𝗯𝗼𝗿𝗻 for data analysis & visualization.
📊 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 algorithms for predictive modeling.
📁 𝗖𝗦𝗩 & 𝗔𝗣𝗜-based datasets from government and academic sources.

𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻
Our 𝗕𝗶𝗴 𝗗𝗮𝘁𝗮-driven study highlights the urgent need for better 𝗳𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 support and educational policies to ensure fair opportunities for all students. This research serves as a foundation for future studies and policymaking in higher education.