Data Analyst with a background in Informatics & Computer Engineering Education.
I build end-to-end analytics solutions—from data wrangling and statistical analysis to interactive dashboards and business recommendations.
- Languages: Python, SQL (PostgreSQL)
- Data & Analytics: Pandas, NumPy, Scikit-learn, XGBoost
- Visualization: Tableau, Power BI, Matplotlib, Seaborn
- Automation & Tools: GitHub Actions, Streamlit, Git, GitHub, VS Code, Google Colab, Kaggle
Analyzed 9,648 Google Play apps to answer: "Where should I invest $100,000?"
Result: Identified Medical category as top performer (2.61¢ per 1K downloads, 4x higher than competitors).
Tools: Python (Pandas, SciPy), Tableau, Statistical Analysis (T-Test, p=0.000014)
Built an executive dashboard tracking $12.4M revenue, $4.50 CAC, and 3.2% conversion rates.
Performed RFM segmentation on 100K+ customers identifying top 10% high-value segment (65% of revenue).
Tools: SQL (PostgreSQL), Python, Tableau
Engineered a daily automated pipeline (GitHub Actions) to validate data integrity - detecting missing values, duplicates, schema drift, and outliers with email alerts and before/after cleaning summaries.
Result: Reduced manual data validation time to zero hours and ensured reliable data for downstream analytics.
Tools: Python (Pandas, NumPy), GitHub Actions, SMTP
Built an interactive Power BI dashboard analyzing 10,000+ retail transactions to uncover sales, profit, and product performance trends.
Result: Delivered a full executive dashboard with $2.3M revenue, $286.4K profit, and a 12.47% profit margin, featuring dynamic slicers and a star schema data model.
Tools: Power BI, Power Query (ETL), Star Schema, DAX
“Turning data into decisions.”