Across my data analytics projects, I’ve turned large, messy datasets into decisions using SQL and Python. For my UPI vs. Cash project, I built an ETL pipeline with seven SQL data models and 15 automated data-quality checks, then forecast monthly cash demand.
In my mobile game A/B test analysis, I found a sample ratio mismatch that changed the retention result and built a reusable statistics toolkit with 13 unit tests. I also developed churn models and a weather-normalization model, and I’m Microsoft Certified as a Power BI Data Analyst Associate (PL-300).
