Projects
Premier League 2024/25 — Player Statistics Analysis
Full-stack data analytics project on 562 Premier League players across 21 clubs. Built a complete pipeline from raw data to interactive Excel dashboard, using MySQL for cleaning and EDA, and Excel for visualization.
- Analyzed 562 players across 21 clubs and 64 nationalities using 57 statistical columns
- Built 15 SQL queries covering top scorers, club rankings, position analysis, and discipline tables
- Used DENSE_RANK(), CTEs, and window functions to rank players within each position
- Delivered scouting-style insight: Salah (29G, 18A) contributes 47 combined goal involvements — 2.3× the league average for forwards, making him highest ROI signing per appearance
- Built interactive Excel dashboard with KPI cards, 4 charts, and full data sheets
-- Key findings -- 🥇 Salah: 29G | 18A | 47 contributions -- 🏆 Liverpool: 72 goals (top club) -- ⚡ FWD: 54% of all goals scored -- 🌍 64 nationalities represented
Global Layoffs — Data Cleaning & EDA
End-to-end SQL project on a real-world global layoffs dataset. Built a full data cleaning pipeline then performed exploratory analysis to surface industry and company-level trends.
- Removed duplicate records using ROW_NUMBER() window functions with full partition logic
- Standardized inconsistent values: trimmed whitespace, unified industry labels, fixed date formats using STR_TO_DATE()
- Handled NULL values via self-JOIN to populate missing industries from matching companies
- Ranked top 5 companies by layoffs per year using DENSE_RANK() inside CTEs
- Built rolling monthly totals using SUM() OVER() for trend visibility
-- Top 5 companies by year WITH Company_year AS ( SELECT company, YEAR(`date`), SUM(total_laid_off) FROM layoffs_staging2 GROUP BY company, YEAR(`date`) ), Ranked AS ( SELECT *, DENSE_RANK() OVER ( PARTITION BY years ORDER BY total_laid_off DESC ) AS Ranking FROM Company_year ) SELECT * FROM Ranked WHERE Ranking <= 5;
Airbnb Seattle — Full Market Analysis Dashboard
End-to-end analytics project joining 3 real-world datasets (3,818 listings · 1M+ calendar records · 84,849 reviews) to uncover pricing patterns, occupancy trends, and seasonal demand cycles across Seattle's short-term rental market.
- Joined Listings + Calendar + Reviews on listing_id to build a unified star-schema model
- ZIP 98134 (SoDo) leads avg pricing at $206.6/night — waterfront proximity drives premium
- Identified January as peak occupancy month (43.7%) driven by conference demand, not tourism — recommended hosts adjust pricing strategy to capture B2B traveller premiums
- Prepared investment recommendation: converting larger properties to short-term rentals in high-density ZIP codes yields 6× higher ROI ($578 vs $95.7/night) — directly actionable for property investors
- August review volume is 4.5× higher than February — validates true seasonal demand
- Built 6-sheet interactive dashboard: Price by ZIP, Map, Revenue/Year, Avg Price/Bedroom, Listings Count, Full Dashboard
-- Avg price by ZIP (top results) SELECT zipcode, ROUND(AVG(price), 1) AS avg_price, COUNT(*) AS listings FROM listings GROUP BY zipcode ORDER BY avg_price DESC;
-- Monthly occupancy rate SELECT MONTH(date) AS month, ROUND(SUM(available='f')*100.0 / COUNT(*), 1) AS occupancy_pct FROM calendar GROUP BY month;
-- Key findings -- 📍 ZIP 98134: $206.6 avg (highest) -- 📅 Jan: 43.7% occupancy (peak) -- 🛏️ 6BR: $578 vs 1BR: $95.7/night
Data Professionals Survey — Global Salary & Job Satisfaction Breakdown
Interactive Power BI dashboard analyzing real survey responses from data professionals worldwide — covering salaries, job satisfaction, career paths, and demographic breakdowns across Data Analysts, Data Scientists, and related roles.
- Analyzed survey data from data professionals across multiple countries and job titles
- Broke down average salaries by role, gender, and country to surface compensation gaps
- Measured job satisfaction scores across salary, work-life balance, and management dimensions
- Visualized career entry difficulty, preferred programming languages, and industry distribution
- Built fully interactive dashboard with slicers for role, country, and gender filtering
-- Avg salary by job title SELECT Job_Title, ROUND(AVG(Salary_USD), 0) AS avg_salary, COUNT(*) AS respondents FROM survey_data GROUP BY Job_Title ORDER BY avg_salary DESC;
-- Satisfaction score by gender SELECT Gender, ROUND(AVG(Salary_Satisfaction), 1) AS avg_salary_satisfaction, ROUND(AVG(WorkLife_Satisfaction), 1) AS avg_worklife_score FROM survey_data GROUP BY Gender;
-- 🌍 Respondents from 20+ countries -- 📊 Roles: Analyst, Scientist, Engineer -- ⭐ Satisfaction tracked across 5 KPIs
Bike Buyers — Customer Analysis Dashboard
Full analysis of customer purchasing behavior across demographics, regions, and income levels. Built an interactive Excel dashboard with dynamic filters to explore who buys bikes and why.
- Analyzed 1,000+ customer records across income, age, education, and commute distance
- Built Pivot Tables to segment buyers by region (Europe, Pacific, North America)
- Created interactive dashboard with Slicers for real-time filtering by gender, marital status, and region
- Identified that customers with 0–1 mile commutes and middle incomes had the highest purchase rates
Global Sales Report — Multi-Country Revenue Dashboard
Comprehensive sales analysis across 6 countries (US, Australia, UK, Germany, France, Canada) spanning 2011–2016, with $85M+ in total revenue tracked.
- Built Pivot Table comparing Revenue, Cost, and Profit by country and year
- Identified the United States as top market at $28M revenue, Australia second at $21M
- Tracked year-over-year growth showing 2015 as peak year at $20M total revenue
- Created dynamic charts to visualize revenue trends across product categories
US Presidents Dataset — Data Cleaning & Standardization
Applied professional data cleaning techniques to a historical US Presidents dataset, fixing date formats, removing duplicates, and standardizing inconsistent entries.
- Identified and removed duplicate records (e.g. Woodrow Wilson duplicate row)
- Converted Excel serial numbers to readable date formats
- Standardized party names and removed extra whitespace using TRIM functions
- Documented all cleaning steps for full reproducibility
HR Employee Lookup System — XLOOKUP & INDEX/MATCH
Built a dynamic employee data retrieval system using XLOOKUP with exact, partial, and multi-column match modes — demonstrating mastery of Excel's most powerful lookup functions.
- Implemented XLOOKUP with exact match, partial match, and wildcard scenarios
- Built multi-column return to fetch Name, Email, Salary, and End Date in one formula
- Added error handling using XLOOKUP's if_not_found parameter ("Not found")
- Applied across employee dataset of 9 staff records with full HR data
Sales & HR Dashboard — Conditional Formatting & Charts
Applied advanced conditional formatting rules and chart design to visualize monthly sales data for Dunder Mifflin paper products and employee HR metrics.
- Applied color-scale conditional formatting to highlight performance outliers
- Tracked 12-month sales across 3 product lines (Paper, Printers, Manila Folders)
- Built bar and line charts for Sales Report with Year End Totals
- Combined HR demographic data with salary ranges for visual reporting
US Fortune 500 — Web Scraping & Revenue Analysis
Built a complete web scraping pipeline in Python to extract the largest US companies by revenue directly from Wikipedia's HTML — no API, no CSV download. Parsed raw HTML tables using BeautifulSoup, structured data with Pandas, and surfaced key revenue and industry insights.
- Scraped 50+ companies across 7 data columns using requests + BeautifulSoup HTML parsing
- Applied browser-like User-Agent headers to handle bot detection on live web pages
- Identified Healthcare (35%) as the dominant industry in the Fortune 500 top 20
- Walmart leads at $648B revenue — 13% ahead of Amazon; Marathon Petroleum top growth at 74.4%