🔍 Exploratory Data Analysis (EDA) – Global Tourism Statistics

Welcome to the Exploratory Data Analysis (EDA) section of the Global Tourism Statistics project! 🧠
This part of the repository focuses on analyzing the Gold Layer datasets to extract insights, validate data integrity, and prepare for reporting and visualization.

The EDA was entirely developed in T-SQL, following a structured, repeatable, and documented workflow.

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📦 Data Source Dependency

The datasets analyzed in this EDA are derived from the Gold Layer of the Global Tourism Data Warehouse project.

To run these SQL scripts successfully, ensure that the Data Warehouse has been built and the Gold Layer views (fact_domestic_tourism, fact_inbound_tourism, fact_outbound_tourism, fact_tourism_industries, fact_sdg, and their related dimensions) are available in SQL Server.

The EDA depends on these structures for querying and validation.

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🗺️ EDA Framework

The EDA process follows a systematic exploration approach, divided into key analytical areas:

1. Database Exploration → Understanding the database structure and objects.
2. Dimensions Exploration → Reviewing unique values and coverage for key dimensions.
3. Date Exploration → Checking time span (1995–2022) and temporal consistency.
4. Measures Exploration → Validating numerical measures and detecting anomalies.
5. Magnitude Analysis → Comparing data magnitude by country, indicator, and year.
6. Ranking Analysis → Identifying top-performing countries and indicators.

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📊 Objectives

The goal of this analysis is to:

• Validate the completeness and quality of the Gold layer.
• Understand temporal, dimensional, and numerical distributions.
• Detect outliers, missing data, and irregularities.
• Support the creation of dashboards and KPIs for global tourism and SDG indicators.

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🧩 EDA Scripts

All EDA modules are written in T-SQL and fully documented.
Each script can be executed independently, using dynamic parameters (@FactViews, @Unit, @Year, @Country, etc.).

Category	Description
🧱 Database Exploration	Lists all objects and columns to understand schema design.
🌍 Dimensions Exploration	Retrieves distinct Countries, Indicators, Years, and Units to verify data coverage.
📅 Date Exploration	Finds first and last year available; checks time range (1995–2022).
📏 Measures Exploration	Calculates totals by unit of measure and flags invalid percentage values.
📈 Magnitude Analysis	Compares total values by Indicator, Country, and Year to identify dominant contributors.
🏆 Ranking Analysis	Produces Top-N/Bottom-N rankings by indicator, country, and year.

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🧠 Insights & Methodology

The EDA is designed to ensure that analytical outputs from the Gold Layer are both valid and meaningful.

Key aspects validated include:

• ✅ Referential integrity (matching dimension keys).
• ✅ Consistent units of measure across indicators.
• ✅ No negative or unrealistic percentage values.
• ✅ Uniform temporal coverage across fact tables.
• ✅ Logical magnitude relationships between inbound, outbound, and domestic datasets.

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🧮 Example Findings

• Year coverage confirmed: 1995 → 2022.
• Units standardized to NUMBER, THOUSANDS, and US$ MILLIONS.
• Outliers identified in SDG and Industries facts where percentages >100%.
• Top tourism spenders (US$ MILLIONS): USA, China, Germany.
• Longest stay averages observed in European and island countries.

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🛠️ Tools & Environment

• SQL Server (T-SQL) — Analysis and validation queries.
• Draw.io — EDA conceptual map (docs/Eda_Map.png).
• GitHub — Repository management and documentation.

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📂 Repository Structure

global-tourism-eda/
│
├── scripts/
│ └── eda/
│ ├── Eda - Database Exploration.sql
│ ├── Eda - Dimensions Exploration.sql
│ ├── Eda - Date Exploration.sql
│ ├── Eda - Measures Exploration.sql
│ ├── Eda - Magnitude Analysis.sql
│ └── Eda - Ranking.sql
│
├── docs/
│ ├── Eda_Map.png
│ ├── types_of_tourism.md
│ ├── tourism_indicators_glossary.md
│ └── ...
│
└── README_EDA.md

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📈 Next Steps

• Conduct advanced KPI analysis in SQL Server on Global Tourism datasets.
• Build Tableau dashboards to visualize key tourism trends and indicators.

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🧾 License

This EDA documentation is part of the Global Tourism Data project and released under the MIT License.

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🌟 About Me

Hi, I’m Daniele Amoroso 👋
HR Generalist transitioning into Data Analytics and Data Science, with a focus on SQL, Python, and AI Automation.

Currently transitioning into the data field by building portfolio projects that combine technical skills with analytical thinking.

Connect: LinkedIn – Daniele Amoroso