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An exploratory data analysis of 8,000+ Netflix titles using Python and Pandas, cleaning and preparing categorical and numerical fields for downstream analysis.
Applied 10+ Pandas techniques, including grouping, filtering, aggregation, sorting, string manipulation, and type conversion, to surface trends across content type, ratings, and release timing.
Produced 6 Matplotlib visualizations covering content type distribution, ratings, movie durations, release trends, and country-level content, then published the complete analysis on GitHub.
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If you run a busy team, I’m an operations and automation specialist.
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I designed a Business Intelligence and financial reporting solution that transforms scattered data from multiple sources into centralized, validated, and decision-ready information.
The solution automates the complete reporting workflow: connecting and consolidating data, cleaning and standardizing information, applying business rules, building a structured data model, calculating financial KPIs, and generating executive dashboards and recurring reports.
Instead of manually combining spreadsheets and preparing reports every reporting period, the workflow creates a repeatable data pipeline that reduces manual work while improving consistency, traceability, and visibility.
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Multi-source data consolidation
Automated ETL and data transformation
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Power Pivot data modeling
Financial KPI calculations
Interactive executive dashboards
Automated and scheduled reporting
Exception and duplicate detection
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Technologies: Excel, Power Query, Power Pivot, VBA, DAX, SQL/CSV data sources and automated reporting workflows.
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All information, companies and metrics displayed in this case study are fictional demo data created exclusively for portfolio purposes.
A dynamic, fully responsive product catalog featuring intuitive category filtering, real-time cart updates, and a clean, minimalist design tailored for a premium brand experience.
