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Project Title

Introduction

This project is an end-to-end data analytics workflow built to extract business insights from a real-world dataset. It begins by loading raw data in Python, explores and cleans the data, then performs SQL-based analysis on a relational database (e.g., PostgreSQL / SQL Server). The insights are visualized in an interactive Power BI dashboard.

The goal is to demonstrate data wrangling, analysis, visualization, and communication skills in a way that's both technically rigorous and recruiter-friendly.


Features

  • Load and preprocess raw data using Python (pandas, NumPy)
  • Perform exploratory data analysis (EDA) with statistical summaries and visualizations
  • Clean and transform data: handle missing values, encode categorical variables, normalize data
  • Store and query data using SQL (PostgreSQL / MySQL / SQL Server)
  • Write SQL queries to answer business questions, compute aggregates, and build views
  • Build a Power BI dashboard for interactive visualization (charts, filters, slicers)
  • Create a detailed report capturing methodology, insights, and recommendations

Tech Stack

Layer Tools / Technologies
Data Processing / EDA Python, pandas, NumPy, matplotlib / seaborn
Database PostgreSQL / SQL Server
BI / Dashboard Power BI
Reporting Microsoft Word / PDF (or Google Docs)
Environment / Dev Jupyter Notebook or VS Code, Git

Usage

  1. Exploratory Analysis & Cleaning
    • Run the Python notebooks to perform EDA and clean the data.
    • Review summary statistics, charts, and understand data quality issues.
  2. Database Analysis
    • Load cleaned data into your SQL database.
    • Execute SQL scripts (provided) to create tables / views and query for insights.
  3. Dashboard Exploration
    • Open the Power BI dashboard file (.pbix).
    • Connect it to your data source (database or CSV).
    • Use interactive filters to explore different dimensions of the data (time, category, region, etc.).
  4. Reporting & Presentation
    • Read through the written report for detailed methodology, visualizations, and business recommendations.
    • Use the Gamma presentation to present key insights to stakeholders in a clean, narrative-driven way.

About

End-to-end analysis of retail consumer behavior to optimize marketing strategy. Built with Python, SQL, and Power BI.

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