Tristram Apurado Jr.

🧠 Analyzing Students' Mental Health - PostgreSQL & Data Analysis

Project Description: Analyzed international students' mental health data from a Japanese university survey to determine if studying in a different country affects mental health. The study found that international students have higher risks of mental health difficulties, with social connectedness and acculturation stress being predictive of depression.

Key Analysis Areas:

• Comparison of depression scores between international and domestic students
• Impact of length of stay on mental health metrics
• Correlation between social connectedness and depression levels
• Analysis of acculturation stress as a predictor of depression
• Effect of language proficiency (Japanese & English) on mental health outcomes

SQL Analysis Performed:

-- Compare depression scores by student type
SELECT inter_dom, 
       ROUND(AVG(todep), 2) as avg_depression,
       ROUND(AVG(tosc), 2) as avg_social_connect,
       ROUND(AVG(toas), 2) as avg_accult_stress
FROM students
GROUP BY inter_dom;

-- Analyze impact of stay duration on mental health
SELECT stay, 
       COUNT(inter_dom) as student_count,
       ROUND(AVG(todep), 2) as average_phq9,
       ROUND(AVG(tosc), 2) as average_scs,
       ROUND(AVG(toas), 2) as average_asss
FROM students
WHERE inter_dom = 'Inter'
GROUP BY stay
ORDER BY stay;

Key Findings:

• International students showed significantly higher depression scores compared to domestic students
• Students with shorter stays (1-2 years) exhibited higher acculturation stress
• Strong correlation found between low social connectedness scores and higher depression levels
• Language proficiency played a moderating role in acculturation stress
• Length of stay showed non-linear relationship with mental health adaptation

🌍 Analyze International Debt Statistics - SQL & World Bank Data

Project Description: Analyzed international debt statistics collected by The World Bank to understand debt patterns across developing countries. The dataset contained information about debt amounts (in USD) owed by countries across various debt indicator categories, providing insights into global economic dependencies and financial obligations.

Key Research Questions:

• What is the total number of distinct countries in the database?
• Which country has the highest total amount of debt?
• Which country has the lowest amount of principal repayments?
• What are the major debt indicator categories?
• How is debt distributed across different regions?

SQL Analysis & Key Queries:

-- 1. Count distinct countries in the database
SELECT COUNT(DISTINCT country_name) as total_distinct_countries
FROM international_debt;

-- 2. Find country with highest total debt
SELECT country_name, SUM(debt) as total_debt
FROM international_debt
GROUP BY country_name
ORDER BY total_debt DESC
LIMIT 1;

-- 3. Find country with lowest principal repayments
SELECT country_name, indicator_name, MIN(debt) as lowest_repayment
FROM international_debt
WHERE indicator_code = 'DT.AMT.DLXF.CD'
GROUP BY country_name, indicator_name
ORDER BY lowest_repayment ASC
LIMIT 1;

-- 4. Analyze debt by indicator categories
SELECT indicator_name, COUNT(*) as country_count,
       ROUND(AVG(debt), 2) as average_debt,
       SUM(debt) as total_debt_by_indicator
FROM international_debt
GROUP BY indicator_name
ORDER BY total_debt_by_indicator DESC
LIMIT 10;

Key Findings & Insights:

• China was identified as the country with the highest total debt amount
• Timor-Leste showed the lowest principal repayments at $825,000 USD
• Analysis revealed distinct debt patterns across different economic sectors
• Long-term external debt constituted a significant portion of total debt
• Debt distribution varied significantly between developing nations
• Infrastructure-related debt indicators were among the most common categories

Technical Skills Demonstrated:

• SQL Aggregation: Used SUM, COUNT, AVG, MIN functions for comprehensive analysis
• Data Filtering: Implemented WHERE clauses for specific indicator analysis
• Grouping & Sorting: Applied GROUP BY and ORDER BY for meaningful data organization
• Data Exploration: Conducted exploratory analysis on World Bank financial data
• Insight Generation: Translated raw data into actionable economic insights

🚴‍♂️ Bike Sales Dashboard - Excel Data Analysis

Project Description: Comprehensive analysis of bike sales data to identify customer demographics and purchasing trends. Developed an interactive dashboard with dynamic filtering capabilities to extract actionable insights into consumer behavior.

Key Skills Demonstrated:

• Data Cleaning: Implemented duplicate removal and data integrity validation
• Data Quality: Applied Data Validation for maintaining consistent data standards
• Data Analysis: Utilized Pivot Tables for customer segmentation and trend analysis
• Data Visualization: Created interactive charts with slicers for dynamic data exploration
• Dashboard Creation: Designed intuitive interface for business intelligence reporting

💰 Personal Budget Tracker – Google Sheets

Project Description: Developed a comprehensive Personal Budget Tracker system for monitoring income, expenses, and cash flow. Implemented automated calculations and interactive dashboard features for real-time financial health assessment.

Key Features & Skills:

• Data Integrity: Established Data Validation protocols and dropdown menus
• Automation: Created Custom Formulas for dynamic financial calculations
• Data Visualization: Designed comprehensive Charts with Conditional Formatting
• User Experience: Built intuitive navigation with filtered data views
• Financial Analysis: Demonstrated advanced spreadsheet management techniques

📈 Data Professional Survey Breakdown - Power BI

Project Description: In-depth analysis of Data Professional Survey data, uncovering industry trends, compensation patterns, and career development insights across the data analytics field.

Key Analysis Areas:

• Compensation analysis by role, experience, and geographic location
• Technology preferences and skill requirements across data roles
• Work-life balance metrics and career satisfaction indicators
• Industry growth patterns and emerging specialization areas

Technical Skills:

• Advanced data transformation using Power Query
• Complex data modeling and relationship management
• DAX measures for sophisticated calculations and KPIs
• Interactive dashboards with drill-through capabilities
• Advanced visualization techniques and conditional formatting

🎓 Academic Stress Level Data Survey

Project Description: Comprehensive analysis of academic stress indicators among student populations, identifying key contributing factors and patterns across diverse educational contexts and demographic segments.