Advanced-Data-Analytics-Project---SQL

A comprehensive collection of SQL scripts for data exploration, analytics, and reporting. These scripts cover various analyses such as database exploration, measures and metrics, time-based trends, cumulative analytics, segmentation, and more. This repository contains SQL queries designed to help data analysts and BI professionals quickly explore, segment, and analyze data within a relational database. Each script focuses on a specific analytical theme and demonstrates best practices for SQL queries.

This project is a hands-on SQL analytics pipeline built to analyze and report retail sales data. It demonstrates data modeling, ETL, analysis, and reporting using structured data and SQL queries.

1. Database Setup

• Created a new database DataWarehouseAnalytics.
• Defined a schema gold for clean data organization.
• Built dimension tables (dim_customers, dim_products) and a fact table (fact_sales) to structure transactional and master data.

2. Data Import & Cleaning

• Used BULK INSERT to load CSV files into the tables.
• Cleaned, validated, and formatted data to ensure consistency.
• Removed duplicates and handled missing values for accurate analysis.

3. Sales & Trend Analysis

• Performed time-based trend analysis (monthly and yearly sales).
• Calculated cumulative sales, average order value, and running totals to track business performance.
• Identified seasonal trends and customer engagement patterns.

4. Performance Analysis

• Evaluated product performance using historical sales data.
• Compared year-over-year sales and calculated average revenue per product.
• Highlighted top-performing products and revenue drivers.

5. Customer Segmentation

• Segmented customers into VIP, Regular, and New based on purchase history and spending.
• Calculated KPIs such as total orders, total sales, recency, and average monthly spending.
• Provided actionable insights for targeted marketing and retention strategies.

6. Reporting Views

• Created reusable SQL views for customer and product reporting:

  • report_customers – aggregates customer data, segments, and KPIs.
  • report_products – summarizes product sales, segments, and revenue metrics.

• These views are ready for integration with BI tools like Power BI or Tableau.

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Key Skills Demonstrated

• SQL: Joins, Window Functions, Aggregations, Subqueries, Views
• Data Engineering: ETL Pipelines, Data Cleaning, Data Integration
• Business Analysis: KPI Tracking, Trend Analysis, Customer Segmentation
• Reporting: Data-ready SQL views for dashboards and visualizations

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Outcome & Impact

• Built an end-to-end analytics pipeline for retail sales data.
• Produced actionable insights for customer retention, sales growth, and product management.
• Delivered a reproducible framework that can be scaled for larger datasets or integrated with BI platforms.

About Me

Hi there! I'm Praneeth Kumar Reddy Pappu. I’m a Data Analyst, and I love working with data

Let's stay in touch! Feel free to connect with me on the following platforms:

[![LinkedIn] https://www.linkedin.com/in/praneethrdy/