This project represents my full-cycle approach to data analytics — from raw data to decision-ready visualization.
I started with a large CSV file containing combined data from multiple marketing channels. Using Python (Pandas), I cleaned the dataset, removed inconsistencies, and isolated the Google Ads records for deeper analysis.
Once the data was structured and filtered, I imported it into MySQL, where I performed analytical processing using complex SQL queries — including JOINs, CTEs, and window functions.
These queries calculated key marketing and engagement metrics such as Click-Through Rate (CTR), Return on Investment (ROI), Daily and Monthly Active Users (DAU/MAU), and churn risk.
This helped uncover which campaigns, countries, and device types contributed most to engagement and conversions.
After completing the SQL-level analytics, I connected the database to Power BI and designed interactive dashboards that displayed campaign ROI trends, audience engagement, and channel effectiveness.
The dashboards were integrated with Google Workspace for team access and automated refreshes, ensuring stakeholders could monitor live performance at any time.
What made this project special for me was how it connected every layer of the data journey — from raw CSV to clean, visual insights that directly supported marketing strategy.
Tech Stack
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Languages: Python, SQL, DAX (Power BI)
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Tools: MySQL, Power BI, Google Workspace
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Libraries: Pandas, NumPy
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Techniques: Data Cleaning, Data Modeling, KPI Analysis, ROI Tracking, Visualization
Key Learnings
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Extracting and cleaning mixed-source campaign data using Python
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Writing optimized SQL queries for KPI calculation and performance metrics
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Designing end-to-end BI workflows from data ingestion to dashboarding
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Presenting marketing data visually to support quick and informed decisions
