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data-driven-decision-making

Here are 32 public repositories matching this topic...

Hospital database system built with Oracle APEX and SQL, featuring an interactive dashboard for real-time insights into patient distribution and doctor availability. Designed to optimize resource management and support hospital administration in data-driven decision-making.

  • Updated Oct 11, 2024
  • PLSQL

This repository contains materials for a Data Analysis Hackathon I designed to provide learners with practical experience in analyzing e-commerce sales and customer insights. Participants utilize tools such as Excel, SQL, Python, and Power BI to explore datasets, uncover trends, answer key business questions, and generate actionable insights.

  • Updated Apr 6, 2025
  • Jupyter Notebook

📊 End-to-end data analytics project analyzing a bank’s loan portfolio to identify profitable segments, high-risk borrowers, and strategic insights using Python, Jupyter Notebook, and data visualization.

  • Updated Dec 1, 2025
  • Jupyter Notebook

This repository contains a Power BI dashboard designed to analyze Amazon's global sales performance. It provides dynamic, interactive visualizations of sales by region, segment, and customer, helping to uncover insights and guide data-driven business decisions.

  • Updated Jan 5, 2025

Developed an interactive Power BI dashboard by integrating transaction and customer data from a SQL database to provide real-time insights. Streamlined data processing to monitor key performance metrics and trends, sharing actionable insights with stakeholders to support data-driven decision-making.

  • Updated Sep 14, 2024

A data-driven approach to designing an optimal Data Science curriculum. This project extracts skills from job postings, applies NLP and clustering techniques (K-Means, Hierarchical, DBSCAN), and maps industry demands to educational recommendations. Uses Python, Scikit-learn, OpenAI embeddings, and Seaborn for visualization.

  • Updated Mar 5, 2025
  • Python

Analyzed lead quality trends to identify top-performing sources and optimize marketing spend. Explored factors like widgets, campaigns, and geography using Python-based analysis and visualizations. Provided actionable insights to improve conversion rates and justify CPL adjustments.

  • Updated Oct 30, 2025
  • Jupyter Notebook

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