Camino de Santiago - Statistics Analysis

🧪 Project Overview

FINAL SLIDE PRESENTATION: https://www.canva.com/design/DAG_nebk9Lk/sLvHiIfI4g30UtyNhWloUw/edit?utm_content=DAG_nebk9Lk&utm_campaign=designshare&utm_medium=link2&utm_source=sharebutton This project analyzes the "The Santiago Way" statistics to uncover trends and insights regarding pilgrims' journeys to Santiago de Compostela. The analysis utilizes a dataset spanning nearly two decades to evaluate key questions proposed in the initial research scope.

The core objectives of this analysis are to determine:

• Growth Trends: Whether the total number of pilgrims is increasing year-over-year and if there's a post-pandemic acceleration.
• Demographic Shifts: Analysis of gender ratios (e.g., increase in female pilgrims) and age distribution (e.g., is the average pilgrim getting younger?).
• Route Popularity: Dominance of the French Way vs. growth of secondary routes like the Portuguese or Primitive ways.
• Nationality Insights: Comparison of European vs. non-European pilgrim behaviors.
• Seasonality & Mode: Impact of summer months and the prevalence of foot vs. bicycle pilgrims.

This repository bridges raw data processing with SQL modeling and Python-based exploratory data analysis (EDA) to answer these questions.

🛠️ Tools

• Python: pandas for data manipulation, cleaning, and initial analysis.
• SQL: Database schema creation and queries for structured analysis (MySQL/MariaDB syntax).
• Tableau: Used for interactive visualizations (Workbook files included).
• Jupyter Notebook: For documenting the workflow from raw data availability to final statistical insights.
• Git & GitHub: Version control and project management.

📁 Repository Structure

The repository is organized as follows:

• data: Contains the CSV datasets.
• notebooks: camino_main.ipynb, The central notebook containing data loading, cleaning, merging logic, and Python-based EDA.
• SQL: camino.sql: SQL script for creating the database schema (camino_santiago) and defining tables (df_routes, df_gender, etc.) along with key analytical queries.
• figures: Stores generated charts and visual assets used in presentations.
• slides: Presentation materials summarizing the findings.

📅 Project Log

Day 1 – Dataset Discovery & Setup

• Identified the "Santiago Way" dataset from Kaggle.
• Defined 10 key research questions (Growth, Demographics, Routes, Seasonality).
• Loaded initial CSV files (camino_origin.csv, camino_routes.csv, etc.) into Python.

Day 2 – Data Cleaning & Preprocessing

• Handled missing values and standardized column names (e.g., identifying 'Pie' as 'Foot', 'Bicicleta' as 'Bicycle').
• Aggregated "Other" categories for transportation and routes to simplify analysis.
• Translated country and route names to English for consistency (e.g., 'Alemania' -> 'Germany').

Day 3 – Relational Database Design (SQL)

• Designed the camino_santiago database schema.
• Created tables for normalized data: df_routes, df_groups, df_countries, df_motives, etc.
• Defined Foreign Keys to establish relationships between the main df_pilgrins table and dimension tables.

Day 4 – Exploratory Analysis (SQL & Python)

• Executed SQL queries.
• Validated the "French Way" dominance hypothesis (>60%).

Day 5 – Visualization & Reporting

• Exported cleaned datasets (df_routes_tableau_map.csv, df_trans_tableau_side.csv) for Tableau.
• Created Tableau visualization workbooks (.twbr) to map pilgrim origins and route density.
• Synthesized findings into the final presentation.

Day 6 – Final Review

• Cleaned up code in camino_main.ipynb.
• Finalized README.md and repository structure.