Daniyal Riaz
Data Analyst | Python, Power BI & SQL Expert
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Automated ETL & Analytics Pipeline (Python)
The Code: Built a custom automation script using Pandas and NumPy to ingest messy raw files, drop null values, and format schemas automatically.
The Automation: You can put your messy and unstructured excel file in this automation script and it will automatically clean that file and make a visualization chart of top 5 best performing products
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E-Commerce Customer Data Cleaning & ETL Pipeline
Description:
Extracted raw transaction records from an e-commerce SQL database, executing initial structural normalization and data filtering within MySQL. Following extraction, migration, and structural staging, conducted advanced data cleansing, schema standardization, and formatting within Microsoft Excel to produce a pristine, production-ready dataset for retail business intelligence.
Workflow & Tools Used:
Data Extraction & Querying: MySQL (Filtering, joining, and database staging)
Data Cleansing & Normalization: Microsoft Excel (Handling missing values, removing duplicate customer records, formatting text data, and verifying data integrity)
Domain Focus: E-Commerce & Retail Metrics
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End-to-End Sales Analytics & Interactive BI Dashboard
Developed a comprehensive data pipeline to clean, structure, and visualize complex transactional data. This interactive dashboard tracks critical business metrics across regions, categories, and payment types—transforming raw data into actionable insights to monitor high-volume performance like $684M in Total Sales and $304M in Net Profit.
Tools Used:
• Data Analytics & Visualization: Power BI, Microsoft Excel
• Database Management: SQL
• Programming & Core Libraries: Python, Pandas, NumPy
• Automation: Custom Python ETL scripting for speed and efficiency
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