Data Analyst | SQL | Python | Data Visualization
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Detail-Oriented | Data-Driven | Business-Aware
🎓 I have a background in Physics and over 5 years of experience in real estate valuation, where I authored 600+ property appraisal reports—honing precision, analysis, and real-world reporting.
📈 My passion for uncovering insights from data led me to transition into Data Analytics, where I built hands-on skills in:
🐍 Python
🧮 SQL & Excel
📊 Power BI & Tableau
☁️ Google BigQuery
🌍 I also studied International Business in Helsinki, gaining an international perspective and a strong foundation in business strategy and cross-cultural thinking.
🤖 I actively integrate AI tools into my workflow to learn faster, work smarter, and enhance problem-solving.
🚀 Currently seeking an entry-level Data Analyst role where I can:
📌 Apply my analytical mindset
🧠 Support data-driven decision-making
🤝 Grow within a collaborative, forward-thinking team
Real-Time Flight Tracking & Airspace Analytics with Python, PostgreSQL, and Power BI
Built a fully automated real-time flight monitoring data pipeline using the OpenSky Network API.
Scheduled GitHub Actions to run every 15 minutes, executing a Python script that fetches,
processes, and stores live aircraft data (e.g., callsign, velocity, altitude, timestamp) into a Neon.tech
PostgreSQL database.
Developed a normalized data model with timestamp-based tracking and key metrics such as velocity in km/h and estimated landing zones.
Connected Power BI Service to the database with auto-refresh configured for live visualization.
Designed an interactive dashboard in Power BI to monitor aircraft over Turkish airspace, with filters by airport proximity, altitude threshold, and update timestamp.
A/B Test: In-App Purchase Pricing Strategy
I designed and implemented an A/B testing project to evaluate the impact of introducing a low-cost
($1) starter pack on mobile game monetization metrics.
Simulated user-level gameplay and purchase data using Python (NumPy, Pandas, UUID) to create realistic experiment conditions.
Defined experiment groups (control vs. variant) and calculated key performance metrics such as conversion rate, ARPU, and churn rate.
Delivered clear and data-driven insights to demonstrate that the $1 starter pack significantly increased ARPU and conversion, without increasing churn.
Documented the entire analysis in a Jupyter Notebook and structured the project as a reproducible
GitHub repository.
Analyzed campaign-level data including impressions, clicks, CTR (Click-Through Rate), CPC (Cost
Per Click), ROMI (Return on Marketing Investment), and ROI (Return on Investment).
Utilized SQL (PostgreSQL) for extracting campaign and audience data from marketing databases.
Performed A/B testing analysis to compare different ad creatives and targeting strategies using
Python (pandas, matplotlib) and Excel.
Identified statistically significant performance differences between campaign variants.
Created a Tableau dashboard to visualize performance trends and enable real-time decisionmaking by the marketing team.
November 2024 - July 2025
Finland | 2012-2015 (missing 40 credits)
B.A. in International Business
Turkey | 2004-2009
B.Sc. in Physics
