MSc Socio-Economic Analytics: UK Census 2011–2021 with Bayesian Modelling & Tableau Dashboards

This repository contains my MSc Data Science project "Exploring Socio-Economic Analytics using Bayesian Modelling and Interactive Tableau Dashboards", conducted at the University of Bristol (Mar–May 2025).
The project investigates education, employment, occupation, and immigration trends across 348 Local Authority Districts (LADs) in England and Wales, comparing the 2011 and 2021 UK Census.

📊 Project Overview

• Objective: To analyze decade-long socio-economic changes and provide interactive dashboards for policy analysts, international students, and education planners.

• Data Sources:

  • 2011 & 2021 UK Census (Nomis, ONS)
  • LAD geocoding & mappings from ONS Geography Codes

• Key Methods:

  • Bayesian Ridge Regression: Imputation of missing 2021 LAD values (31 districts)
  • Dimensionality Reduction: UMAP & t-SNE for 2D socio-economic clustering
  • KMeans Clustering: Grouping LADs (k=5) to reveal regional shifts
  • Interactive Dashboards (Tableau): Education, Employment, Immigration, Cluster Transitions

---

⚙️ Methodology

1. Data Integration:

   • Standardised LAD codes across 2011 & 2021
   • Harmonised 10 socio-economic datasets (education, employment, occupation, migration)

2. Imputation (Bayesian Ridge Regression):

   • Predicted missing LAD values using 2011 trends
   • Quantified coefficient uncertainty with credible intervals
   • Aligned imputed values to population totals

3. Clustering & Dimensionality Reduction:

   • UMAP & t-SNE projections of 28 features
   • KMeans clustering (k=5) to identify regional shifts

4. Visual Analytics (Tableau):

   • Choropleth maps
   • Interactive filters (Year, Cluster, LAD, Continent)
   • Linked bar charts, highlight tables, and tooltips

---

🖼️ Dashboards & Visuals

1. Education & Employment Change (2011 vs 2021)

2. Immigration & Skilled Employment

3. UMAP Clustering of LADs

4. t-SNE Clustering of LADs

---

🔑 Key Insights

• Education: Level 4+ qualifications increased nationally (27.06% → 33.30%).
• Occupations: Rise in professional roles (16.88% → 19.59%), decline in trades & mechanical jobs.
• Employment: Full-time employment decreased (38.76% → 34.10%), partly due to Brexit & pandemic.
• Immigration: Highest foreign-born populations in Westminster, Camden, City of London; strong alignment between education and skilled employment.
• Clustering: UMAP & t-SNE revealed LAD groupings with similar socio-economic profiles, highlighting regional shifts between 2011 and 2021.

---

🛠️ Tools & Technologies

• Python: Pandas, Scikit-learn, UMAP, t-SNE, KMeans, Matplotlib, Seaborn
• Tableau: Interactive dashboards, choropleth maps, highlight tables
• Data Sources:
  • Nomis Census Data
  • ONS Statistics
  • ONS Geography Codes

---

📚 References

1. Munzner, T. (2014). Visualization Analysis and Design. CRC Press.
2. Ware, C. (2020). Information Visualization: Perception for Design (4th ed.). Morgan Kaufmann.
3. Tufte, E. R. (2001). The Visual Display of Quantitative Information (2nd ed.). Graphics Press.
4. Fry, B. (2007). Visualizing Data. O’Reilly Media.
5. Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O’Reilly Media.
6. Raschka, S., & Mirjalili, V. (2019). Python Machine Learning (3rd ed.). Packt.
7. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer.
8. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
9. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
10. McInnes, L., Healy, J., & Melville, J. (2018). UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. arXiv:1802.03426.
11. van der Maaten, L., & Hinton, G. (2008). Visualizing Data using t-SNE. JMLR, 9(Nov), 2579–2605.
12. Tipping, M. E. (2001). Sparse Bayesian Learning and the Relevance Vector Machine. JMLR, 1(Jun), 211–244.
13. Nomis Census Data (2011 & 2021). https://www.nomisweb.co.uk/
14. Office for National Statistics (ONS). https://www.ons.gov.uk/
15. ONS Geography Codes & Lookups. https://geoportal.statistics.gov.uk/
16. Tableau Desktop (2025). https://www.tableau.com/

---

👩‍🎓 Author

Thrisha Rajkumar
MSc Data Science – University of Bristol
🔗 LinkedIn

---